Sparse code shrinkage: denoising by nonlinear maximum likelihood estimation
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
Journal of Cognitive Neuroscience
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Super Symmetric Tensor Factorization
Neural Information Processing
Fast and Efficient Algorithms for Nonnegative Tucker Decomposition
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Decompositions of a Higher-Order Tensor in Block Terms—Part I: Lemmas for Partitioned Matrices
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications
Tensor Decompositions and Applications
SIAM Review
IEEE Transactions on Audio, Speech, and Language Processing
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Detecting the Number of Clusters in n-Way Probabilistic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Computer Science and Technology
A survey of multilinear subspace learning for tensor data
Pattern Recognition
A Block Component Model-Based Blind DS-CDMA Receiver
IEEE Transactions on Signal Processing
Convolutive Speech Bases and Their Application to Supervised Speech Separation
IEEE Transactions on Audio, Speech, and Language Processing
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Multilinear algebra of the higher-order tensor has been proposed as a potential mathematical framework for machine learning to investigate the relationships among multiple factors underlying the observations. One popular model Nonnegative Tucker Decomposition (NTD) allows us to explore the interactions of different factors with nonnegative constraints. In order to reduce degeneracy problem of tensor decomposition caused by component delays, convolutive tensor decomposition model is an appropriate model for exploring temporal correlations. In this paper, a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model is proposed using an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition. The patterns across columns in convolutive tensor model are investigated to represent audio and image considering multiple factors. We employ the K-CNTD algorithm to extract the shift-invariant sparse features in different subspaces for robust speaker recognition and Alzheimer's Disease(AD) diagnosis task. The experimental results confirm the validity of our proposed algorithm and indicate that it is able to improve the speaker recognition performance especially in noisy conditions and has potential application on AD diagnosis.