anhhuyphan

Google Scholar: https://scholar.google.co.jp/citations?user=pJb7n9EAAAAJ&hl=en&oi=ao
Researchgate: https://www.researchgate.net/profile/Anh_Huy_Phan

Conduct research on the following main projects

Tensor decompositions (TDs) and novel applications: o Deal with most challenging problems in tensor decompositions, e.g., high computational cost of algorithms, the second-order optimization method is inadequate, a measure of performance of algorithms, massive tensor decomposition, tensor deflation.

o Develop robust algorithms with low computational costs for various low-rank tensor decompositions of large-scale data, including the Candecomp/Parafac, Tucker tensor decompositions, low-rank tensor deconvolution, tensor diagonalization, Kronecker tensor decomposition, tensor deflation, feature extraction for multiway data.

o We contributed state-of-the-art algorithms to the tensor analysis, especially for the Candecomp/Parafac tensor decomposition. Source codes of the developed algorithms are provided in the package TENSORBOX.

Blind Sources Separation. We developed the algorithms for blind source separation based on independent component analysis, nonnegative matrix factorization and tensor network decomposition. Our methods consist of two steps: tensorization of the mixture signals to yield tensors, low-rank tensor decomposition to retrieve the hidden sources.

Brain Computer interface (BCI): we applied tensor decomposition to extract relevant features for EEG signals in BCI system.

Nonlinear system identification based on multivariate polynomial regression, Volterra-type models, in which parameters are represented in Tensor network format. Our models can be applied to regression, discriminant analysis, support vector machines, deep learning, data fusion and feature combination.

• Other research:

    o Early detection of Alzheimer disease
    o Deconvolution of large-scale Calcium imaging data
o Coding of faces by components with complexity constraints

SELECTED PUBLICATIONS

[1] Cichocki and A.-H. Phan. “Fast local algorithms for large-scale nonnegative matrix and tensor factorizations”. IEICE (invited paper), E92-A(3):708–721, March 2009.https://pdfs.semanticscholar.org/4229/f467b059188fc7a1234016a3c80557fa7df0.pdf

[2] A.-H. Phan and A. Cichocki: “Tensor Decompositions for Feature Extraction and Classification of High Dimensional Datasets”, Nonlinear Theory and Its Applications, IEICE (invited paper) October 2010, 1 (1): 37-68. https://www.jstage.jst.go.jp/article/nolta/1/1/1_1_37/_article/-char/ja/

[3] A.-H. Phan, P. Tichavsky and A. Cichocki, “Tensor deflation for CANDECOMP/PARAFAC. Part 1: alternating subspace update algorithm”, IEEE Transactions on Signal Processing, 63(12), pp. 5924-5938, 2015. http://ieeexplore.ieee.org/abstract/document/7163349/

[4] A.-H. Phan, P. Tichavsky and A. Cichocki, “Tensor deflation for CANDECOMP/PARAFAC. Part 2: Initialization and error analysis”, IEEE Transactions on Signal Processing, 63(12), pp. 5939-5950, 2015. http://ieeexplore.ieee.org/document/7163358/

[5] A.-H. Phan, P. Tichavský, A. Cichocki: “Low complexity damped Gauss-Newton algorithms for parallel factor analysis”, SIAM J. Matrix Analysis and Applications 34(1): 126-147, 2013. http://epubs.siam.org/doi/abs/10.1137/100808034/

Monographs, Books
[6] A. Cichocki, N. Lee, I. Oseledets, A.-H. Phan, Q. Zhao and D. Mandic. “Tensor Networks for Dimensionality Reduction and Large-Scale Optimization. Part 1: Low-Rank Tensor Decompositions”, Foundations and Trends in Machine Learning, vol. 9, no. 4-5 (2016), pp. 249-429.

[7] A. Cichocki, A.-H. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama and D. Mandic. “Tensor Networks for Dimensionality Reduction and Large-Scale Optimization. Part 2: Applications and Future Perspectives”, Foundations and Trends in Machine Learning, vol. 9, no. 6 (2016) 431-673.

[8] A. Cichocki, R. Zdunek, A.-H. Phan, and S. Amari. Non-negative Matrix and Tensor Factorizations Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. John Wiley, New York, 2009.

Patent
A. Cichocki, A.-H. Phan. Brain Wave Analysis Apparatus, Brain Wave Analysis Method, Program and Recording Medium.WO Patent 2,012,133,185, 2012.

Reviewed Journal Publications

[10] A. -H. Phan, P. Tichavský, and A. Cichocki, “Error preserving correction for CPD and bounded-norm CPD” , arXiv preprint arXiv: 1709.08349, 2017.

[11] A. -H. Phan, P. Tichavský, and A. Cichocki, “Best rank-one tensor approximation and parallel update algorithm for CPD”, arXiv preprint arXiv: 1709.08336, 2017.

[12] A. -H. Phan, P. Tichavský, and A. Cichocki, “Tensor networks for LVA. Part III: CPD of high order tensors”, arXiv preprint, 2017.

[13] A. -H. Phan, M. Yamagashi, D. Mandic and A. Cichocki, “Quadratic programming over ellipsoids and its applications to constrained linear regression and tensor decomposition”, arXiv preprint, 2017.

[14] P. Tichavský, A. -H. Phan, and A. Cichocki, “Non-orthogonal tensor diagonalization”, Signal Processing 138 (2017), pp. 313–320.

[15] P. Tichavský, A. -H. Phan, and A. Cichocki, “Numerical CP decomposition of some difficult tensors”, Journal of Computational and Applied Mathematics, vol. 317, pp. 362–370, 2017.

[16] P. Tichavsky, A.-H. Phan, and A. Cichocki, “Partitioned alternating least squares technique for canonical polyadic tensor decomposition,” IEEE Signal Processing Letters, peer review, vol. 23, no. 7, pp. 993–997, July 2016.

[17] A.-H. Phan, A. Cichocki, A. Uschmajew, P. Tichavsky, G. Luta, D. Mandic, Tensor networks for latent variable analysis. Part I: Algorithms for tensor train decomposition, arXiv preprint arXiv:1609.09230

[18] A.-H. Phan, P. Tichavsky and A. Cichocki, “Tensor deflation for CANDECOMP/PARAFAC. Part 1: alternating subspace update algorithm”, IEEE Transactions on Signal Processing, 63(12), pp. 5924-5938, 2015.

[19] A.-H. Phan, P. Tichavsky and A. Cichocki, “Tensor deflation for CANDECOMP/PARAFAC. Part 2: Initialization and error analysis”, IEEE Transactions on Signal Processing, 63(12), pp. 5939-5950, 2015.

[20] A.-H. Phan, P. Tichavsky and A. Cichocki, “Tensor deflation for CANDECOMP/PARAFAC. Part 3: Rank splitting”, ArXiv e-prints 2015, [Online]. Available: http://arxiv.org/abs/1506.04971.

[21] A. Cichocki, D. Mandic, A.-H. Phan, C. Caiafa, G. Zhou, Q. Zhao and L. De Lathauwer, “Tensor decompositions for signal processing applications from two-way to multiway component analysis”, IEEE Signal Processing Magazine, peer review, 32 (2), pp. 145–163, 2015.

[22] R. Zdunek, A.-H. Phan and A. Cichocki, “Image classification with nonnegative matrix factorization based on spectral projected gradient”, P. Koprinkova-Hristova, et al. (eds.), Artificial Neural Networks, Springer Series in Bio-/Neuroinformatics, pp. 31-50, 2015.

[23] A.-H. Phan, P. Tichavský, A. Cichocki: “Fast alternating algorithms for high dimensional CANDECOMP/PARAFAC tensor factorization”, IEEE Transactions on Signal Processing, 2013, 61 (19), 4834-4846. 2013.

[24] A.-H. Phan, P. Tichavský, A. Cichocki: “CANDECOMP/PARAFAC decomposition of high-order tensors through tensor reshaping”, IEEE Transactions on Signal Processing, 61 (19), 4847 – 4860. 2013.

[25] A.-H. Phan, P. Tichavský, A. Cichocki: “Low complexity damped Gauss-Newton algorithms for parallel factor analysis”, SIAM J. Matrix Analysis and Applications 34(1): 126-147, 2013.

[26] P. Tichavský, A.-H. Phan, Z. Koldovský, “Cramer-Rao induced bounds for CANDECOMP/PARAFAC tensor decomposition”, IEEE Transactions on Signal Processing 61(8): 1986-1997, 2013.

[27] Z. Koldovský, P. Tichavský, A.-H. Phan, A. Cichocki, “A two-stage MMSE beamformer for underdetermined signal separation”, IEEE Signal Processing Letters, 20 (12), 1227-1230, 2013.

[28] F. Cong, A.-H. Phan, P. Astikainen, Q. Zhao, Q. Wu, J. K. Hietanen, T. Ristaniemi, A. Cichocki: “Multi-domain feature extraction for small event-related potentials through nonnegative multi-way array decomposition from low dense array EEG”. International Journal of Neural Systems. 23(2), 2013.

[29] F. Cong, A.H. Phan, Q. Zhao, T. Huttunen-Scott, J. Kaartinen, T. Ristaniemi, H. Lyytinen, and A. Cichocki , “Benefits of multi-domain feature of mismatch negativity extracted by nonnegative tensor factorization from EEG collected by low density array”, International Journal of Neural Systems, 22(6), 2012.

[30] A.-H. Phan, A. Cichocki: “Seeking an appropriate alternative least squares algorithm for nonnegative tensor factorizations”. Neural Computing and Applications, 22(6) (2012).

[31] A.-H. Phan, A. Cichocki: “Extended HALS algorithm for nonnegative tucker decomposition and its applications for multi-way analysis and classification”, Neurocomputing 74(11): 1956-1969, 2011.

[32] A.-H. Phan and A. Cichocki: “PARAFAC algorithms for large-scale problems”, Neurocomputing, 74(11): 1970-1984, 2011.

[33] A.H Phan and A. Cichocki: “Tensor Decompositions for Feature Extraction and Classification of High Dimensional Datasets”, Nonlinear Theory and Its Applications, IEICE (invited paper) October 2010, 1 (1): 37-68.

[34] A.-H. Phan, A. Cichocki, and T. Vu-Dinh, “Nonnegative DEDICOM based on tensor decompositions for social networks exploration”, ICONIP & Australian Journal of Intelligent Information Processing Systems , vol. 12, no. 1, pp 10-15, 2010.

[35] R. Zdunek, A.-H. Phan, and A. Cichocki, “Damped Newton iterations for nonnegative matrix factorization”, ICONIP & Australian Journal of Intelligent Information Processing Systems, vol. 12, no. 1, pp 16-22, 2010.

[36] Cichocki and A.-H. Phan. “Fast local algorithms for large-scale nonnegative matrix and tensor factorizations”. IEICE (invited paper), E92-A(3):708–721, March 2009.

[37] A.Cichocki, Y. Washizawa, T. Rutkowski, H. Bakardjian, A.-H. Phan, S. Choi, H. Lee, Q. Zhao, L. Zhang, and Y. Li. “Noninvasive BCIs: Multiway signal-processing array decompositions”. IEEE Computer, 41(4):34–42, 2008.

Peer-reviewed International Conference Papers Published in Proceedings

[38] P. Tichavskysky, A.-H. Phan and A. Cichocki, Under-Determined Tensor Diagonalization for Decomposition of Difficult Tensors, CAMSAP 2017 – 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pp. 263-266.

[39] A.-H. Phan, P. Tichavsk., and A. Cichocki, Blind source separation of single-channel mixture using tensorization and tensor diagonalization, 2017, International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Lecture Notes in Computer Science, vol. 10169, p. 36-46, 2017.

[40] A.-H. Phan, M. Yamagishi, and A. Cichocki, An augmented Lagrangian algorithm for decomposition of symmetric tensors of order-4, 2017, IEEE International Conference on Acoustics, Speech and Signal Processing, 2547-2551.

[41] A.-H. Phan, P. Tichavsk., and A. Cichocki, Partitioned hierarchical alternating least squares algorithm for CP tensor decomposition, 2017, IEEE International Conference on Acoustics, Speech and Signal Processing, 2542-2546.

[42] N. Lee, A.-H. Phan, F. Cong, A. Cichocki, Nonnegative tensor train decompositions for multi-domain feature extraction and clustering, International Conference on Neural Information Processing (ICONIP), Lecture Notes in Computer Science, vol. 9949, pp.87-95, 2016 (best paper award).

[43] A.-H. Phan, P. Tichavsk. and A. Cichocki, Rank-one tensor injection: a novel method for canonical polyadic tensor decomposition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), peer review, pp. 2549-2553, 2016.

[44] A. -H. Phan , P. Tichavsk., and A. Cichocki, Rank splitting for CANDECOMP/PARAFAC, in Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA, Lecture Notes in Computer Science, vol. 9237, pp.31-40, Springer, 2015.

[45] A. -H. Phan , P. Tichavsk., and A. Cichocki, “Low rank tensor deconvolution”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), peer review, pp. 2169 – 2173, 2015.

[46] P. Tichavsk., A.-H. Phan, and A. Cichocki, “Two-sided diagonalization of order-three tensors”, in 23rd European Signal Processing Conference (EUSIPCO), peer review, pp. 2169–2173 pp-998-1002, 2015.

[47] A.-H. Phan, P. Tichavsk., and A. Cichocki, “Deflation method for CANDECOMP/PARAFAC tensor decomposition”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), peer review, pp. 6736-6740, 2014.

[48] A.-H. Phan, A. Cichocki, and P. Tichavsk. , “On fast algorithm for Tucker decomposition with orthogonality constraints”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), peer review, pp. 6766–6770, 2014.

[49] R. Zdunek, A.-H. Phan, A. Cichocki, GNMF with Newton-Based Methods, Artificial Neural Networks and Machine Learning–ICANN, 2013, Lecture Notes in Computer Science, vol. 8131, pp.90-97.

[50] P. Tichavsk., A.-H. Phan, A. Cichocki, A further improvement of a fast damped GAUSS-NEWTON algorithm for CANDECOMP/PARAFAC tensor decomposition, Proc. of IEEE Int. Conf. Acoustics, Speech, Signal Processing,

ICASSP, pp.5964-5968, 2013.

[51] A. J. Brockmeier, J. C Principe, A.-H. Phan, A. Cichocki, A greedy algorithm for model selection for tensor decompositions, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6113-6117, 2013.

[52] A.-H. Phan, A. Cichocki, Petr Tichavsk., R. Zdunek and S. Lehky, From basis components to complex structural patterns, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3228-3232, 2013.

[53] A.-H. Phan, A. Cichocki, P. Tichavsk., G. Luta, A. Brockmeier, Tensor completion through multiple Kronecker product decomposition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3233-3237, 2013.

[54] F. Cong , A.-H. Phan, Q. Zhao, Q. Wu, T. Ristaniemi , A. Cichocki, Feature extraction by nonnegative Tucker decomposition from EEG data including testing and training observations. International Conference on Neural Information Processing (ICONIP) 2012, Lecture Notes in Computer Science, vol. 7665, pp. 166-173.

[55] A.-H. Phan, A. Cichocki, P. Tichavsk., Z. Koldovsk.: On connection between the convolutive and ordinary nonnegative matrix factorizations. International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), Lecture Notes in Computer Science, vol 7191, 2012: 288-296.

[56] A.-H. Phan, A. Cichocki, P. Tichavsk., D. P. Mandic, K. Matsuoka: On Revealing Replicating Structures in Multiway Data: A novel tensor decomposition approach. International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), Lecture Notes in Computer Science, vol 7191, 2012: 297-305.

[57] F. Cong, A.-H. Phan, P. Astikainen, Q. Zhao, J. K. Hietanen, T. Ristaniemi, A. Cichocki: Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs 14 Electrodes EEG Data. LVA/ International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), Lecture Notes in Computer Science, vol 7191, 2012: 502-510.

[58] Z. Koldovsk., A.-H. Phan, P. Tichavsk., and A. Cichocki, “A treatment of EEG motor imagery data by underdetermined blind source separation,” 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), Bucharest, 2012, pp. 1484-1488.

[59] F. Cong, A.-H. Phan, Q. Zhao, A. K. Nandi, V. Alluri, P. Toiviainen, H. Poikonen, M. Huotilainen, A. Cichocki, T. Ristaniemi. Analysis of ongoing EEG

elicited by natural music stimuli using nonnegative tensor factorization. 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), Bucharest, 2012, pp. 494-498.

[60] A.-H. Phan, P. Tichavsk., A. Cichocki, and Z. Koldovsk., Low-rank blind nonnegative matrix deconvolution, Proc. of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1893-1896, ISBN: 978-1-4673-0044-5, Kyoto, Japan, March 2012.

[61] A. Onishi, A.-H. Phan, K. Matsuoka, A. Cichocki, Tensor classification for P300-based brain computer interface . In: Proceedings of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 581-584 (2012).

[62] A.-H. Phan, A. Cichocki, K. Matsuoka, and J. Cao, Novel hierarchical ALS algorithm for nonnegative tensor factorization. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp: 1984-1987, 2011.

[63] A.-H. Phan, P. Tichavsk., A. Cichocki, Fast damped Gauss-Newton Algorithm for sparse and nonnegative tensor factorization. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp: 1987-1991, 2011.

[64] A.-H. Phan, P. Tichavsk., A. Cichocki, Levenberg–Marquardt algorithm for nonnegative TUCKER decomposition. 2011 IEEE Statistical Signal Processing Workshop (SSP), Nice, 2011, pp. 665-668.

[65] Z. Koldovsk., P. Tichavsk. and A.-H. Phan, Stability analysis and fast damped-Gauss-Newton algorithm for INDSCAL tensor decomposition, 2011 IEEE Statistical Signal Processing Workshop (SSP), Nice, 2011, pp. 581-584.

[66] A.-H. Phan, A. Cichocki, R. Zdunek, and T. Vu Dinh, Novel alternating least squares algorithm for nonnegative matrix and tensor factorizations. International Conference on Neural Information Processing (ICONIP) 2010, Lecture Notes in Computer Science, vol. 6443, pp. 262-269.

[67] A.-H. Phan, A. Cichocki, and T. Vu-Dinh, A tensorial approach to single trial recognition for brain-computer interface. The 2010 International Conference on Advanced Technologies for Communications, Ho Chi Minh City, 2010, pp. 138-141.

[68] A.-H. Phan, A. Cichocki, and T. Vu-Dinh, Classification of scenes based on multiway feature extraction. The 2010 International Conference on Advanced Technologies for Communications, Ho Chi Minh City, 2010, pp. 142-145.

[69] F. Cong, I. Kalyakin, A.-H. Phan, A. Cichocki, T. Huttunen-Scott, H. Lyytinen, T. Ristaniemi: Extract mismatch negativity and P3a through two-dimensional nonnegative decomposition on time-frequency represented event-related potentials. Advances in Neural Networks. ISNN 2010. Lecture Notes in Computer Science, vol 6064, pp. 385-391.

[70] F. Cong, A.-H. Phan, H. Lyytinen, T. Ristaniemi, A. Cichocki, Classifying healthy children and children with attention deficit through features derived from sparse and nonnegative tensor factorization using event-related potential. International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), Lecture Notes in Computer Science, vol 6365, pp. 620-628, 2010.

[71] F. Cong, A.-H. Phan, A. Cichocki, H. Lyytinen and T. Ristaniemi, Identical Fits of nonnegative matrix/tensor factorization may correspond to different extracted event-related potentials, Proc. International Joint Conference on Neural Networks 2010 (IEEE World Congress on Computational Intelligence), Barcelona, Spain, July 18-23, 2010, pp. 2260-2264.

[72] S. Sanei, A.-H. Phan, J-L. Lo, V. Abolghasemi, A. Cichocki. A compressive sensing approach for progressive transmission of images. Digital Signal Processing, 2009, 16th International Conference on, 5-7 July 2009, page(s):1 – 5.

[73] A.-H. Phan and A. Cichocki. Analysis of interactions among hidden components for Tucker model. Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, pp. 154-159.

[74] A.-H. Phan and A. Cichocki. Local learning rules for nonnegative Tucker decomposition. Lecture Notes in Computer Science, vol. 5863, pp. 538-545, 2009.

[75] A.-H. Phan and A. Cichocki. Advances in PARAFAC using parallel block decomposition. International Conference on Neural Information Processing (ICONIP), Lecture Notes in Computer Science, vol. 5863, pp. 323-330, 2009.

[76] Q. Zhao, C. F. Caiafa, A. Cichocki, L. Zhang, and A.-H. Phan. Slice oriented tensor decomposition of EEG data for feature extraction in space, frequency and time domains. International Conference on Neural Information Processing (ICONIP), Lecture Notes in Computer Science, 2009, vol.5863, 221-228, 2009.

[77] A.-H. Phan and A. Cichocki. Fast nonnegative tensor factorization for very large-scale problems using two-stage procedure. 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, Dutch Antilles, 2009, pp. 297-300.

[78] A.-H. Phan and A. Cichocki. Block decomposition for very large-scale nonnegative tensor factorization. 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, Dutch Antilles, 2009, pp. 316 – 319.

[79] A. Cichocki, A.-H. Phan, and C. Caiafa. Flexible HALS algorithms for sparse non-negative matrix/tensor factorization, Machine Learning for Signal Processing (MLSP) 2008, pp. 73 – 78.

[80] A.-H. Phan, A. Cichocki and K. S. Nguyen: “Simple and efficient algorithm for distributed compressed sensing”, Machine Learning for Signal Processing (MLSP) 2008, pp. 61 – 66.

[81] A.-H. Phan, A. Cichocki: “Multi-way nonnegative tensor factorization using fast hierarchical alternating least squares algorithm (HALS)”, In proceeding of the 2008 International Symposium on Nonlinear Theory and its Applications (NOLTA2008), Budapest, Hungary, Sept 7-10.

[82] A.-H. Phan, A. Cichocki and K. S. Nguyen: “Novel approach for multidimensional data reconstruction and compression”, In proceeding of the 2008 International Conference on Advanced Technologies for Communications and REV’08, Hanoi.

[83] A. Cichocki, A.-H. Phan, R. Zdunek, and L.-Q. Zhang. Flexible component analysis for sparse, smooth, nonnegative coding or representation. In Lecture Notes in Computer Science, LNCS-4984, volume 4984, pages 811–820. Springer, 2008.

[84] A.-H. Phan and A. Cichocki. Fast and efficient algorithms for nonnegative Tucker decomposition. In Proc. of The Fifth International Symposium on Neural Networks, Springer LNCS-5264, pages 772–782, Beijing, China, 24–28, September 2008.

[85] A.-H. Phan, K.-S. Nguyen, A New Robust Algorithm for ICA Based on α-Renyi Entropy and Minimum Spanning Tree, REV’06 (10th Vietnam Conference on Radio & Electronics), SESSION 4. SIGNAL PROCESSING & CODING, 2006.

[86] A.-H. Phan, K.-S. Nguyen, New Quality Measure for ICA: Simple, Effective and Intuitive, REV’06 (10th Vietnam Conference on Radio & Electronics), SESSION 4. SIGNAL PROCESSING & CODING, 2006.

[87] L. A. Nguyen, A.-H. Phan, K.-S. Nguyen, A Video Stabilization Proposal Using a Nine-Parameter Affine Model, REV’04 (9th Vietnam Conference on Radio & Electronics), Nov. 27-28, 2004, pp. 286-290.

[88] A.-H. Phan, L. A. Nguyen, K.-S. Nguyen, A New Affine Transform Model: 9-Parameter Model, Proceedings of the 2004 International symposium on advanced Science and Engineering, HCM City University of Technology and Pukyong National University (Korea), May, 20-21, 2004, pp. 8-11.

[89] K.-S. Nguyen, A.-H. Phan, Th. T. Dang, Gaussian Noise Filtering on Video By Coring Technique and Wavelet Transform, Proceedings of the 8th Conference on Science and Technology, 25-26 April, 2002, pp. 103-108 (VNU-HCM).

[90] K.-S. Nguyen, A.-H. Phan, L. A. Nguyen, Digital Video Restoration System (DVRS) for Broadcasting Purpose, REV’02 (8th Vietnam Conference on Radio & Electronics), No. 2-3, 2002, pp. 221-224.

Toolboxes

[1] A. Cichocki, S Amari, K Siwek, T Tanaka, A.H. Phan, R Zdunek, S Cruces, ICALAB toolboxes, url: http://www. bsp. brain. riken. jp/ICALAB.

[2] A.-H. Phan, NFEA toolbox: A toolbox for N-way Feature Extraction and Applications, url: http://www. bsp. brain. riken. jp/~phan/nfea/.

[3] A.-H. Phan, P. Tichavsk. , and A. Cichocki, Tensorbox: a tensor toolbox for tensor decompositions, url: http://www. bsp. brain. riken. jp/~phan/, 2013.

Science Projects

[1] A.-H. Phan (leader and principal researcher) “Data and Application Broadcasting for Digital Television”, 2007 (Excellent evaluation).

[2] A.-H. Phan (leader and major researcher) “Vietnamese Electronic Program Guide for Vietnam Television”, Vietnam Television project, Aug 2006. Excellent evaluation.

[3] A.-H. Phan (leader and principal researcher) “Data and Application Broadcasting for Digital Television”, 2007 (Excellent evaluation).

[4] K. S. Nguyen (leader), A.-H. Phan (principal researcher)…”Blind Sources Separation with The Number of Signal Sources is More Than That of Sensors Using ICA (Independent Component Analysis)”, International University, Vietnam National University-Hochiminh City, 2005, Good evaluation.

[5] A.-H. Phan (leader and principal researcher) Nguyen Kim Sach, Dang Thanh Tin … “The system for automatically recognizing and reducing the noise of video”, Vietnam Television project, Jan 2004, Excellent evaluation.

[6] K. S. Nguyen (leader), Phan Anh Huy (principal researcher), Dang Thanh Tin … “Designing the system for video restoration and improvement”, Vietnam Television project, July 2001, Good evaluation.

Workshop, Invited Talk

[1] S. R. Lehky, A. -H. Phan, A. Cichocki, and Keiji Tanaka, Coding of Faces by Tensor Components, The 12th International Neural Coding Workshop, 2016, https://events.uni-koeln.de/frontend/index.php?folder_id=106.

[2] P. Tichavský, A. -H. Phan, and A. Cichocki, “Numerical CP decomposition of some difficult tensors”, in TDA workshop, 2016.

[3] A.-H. Phan, Tensor decompositions for multiway classification. SIAM workshop, TDA 2010, Bari, Italy.

[4] A. Cichocki, and A.-H. Phan, Multi-way array (tensor) factorizations and decompositions and their potential application, ICA 2009, Paraty, Brazil (invited talk).

Best paper award

N. Lee, A.-H. Phan, F. Cong, A. Cichocki, Nonnegative tensor train decompositions for multi-domain feature extraction and clustering, International Conference on Neural Information Processing (ICONIP), Lecture Notes in Computer Science, vol. 9949, pp.87-95, 2016 (best paper award).

RIKEN Research and Technology Incentive Award, 2018