Preface.
Acknowledgments.
Glossary of Symbols and Abbreviations.
1 Introduction ? Problem Statements and Models.
1.1 Blind Source Separation and Linear Generalized Component
Analysis.
1.2 Matrix Factorization Models with Nonnegativity and Sparsity
Constraints.
1.2.1 Why Nonnegativity and Sparsity Constraints?
1.2.2 Basic NMF Model.
1.2.3 Symmetric NMF.
1.2.4 Semi-Orthogonal NMF.
1.2.5 Semi-NMF and Nonnegative Factorization of Arbitrary
Matrix.
1.2.6 Three-factor NMF.
1.2.7 NMF with Offset (Affine NMF).
1.2.8 Multi-layer NMF.
1.2.9 Simultaneous NMF.
1.2.10 Projective and Convex NMF.
1.2.11 Kernel NMF.
1.2.12 Convolutive NMF.
1.2.13 Overlapping NMF.
1.3 Basic Approaches to Estimate Parameters of Standard NMF.
1.3.1 Large-scale NMF.
1.3.2 Non-uniqueness of NMF and Techniques to Alleviate the
Ambiguity Problem.
1.3.3 Initialization of NMF.
1.3.4 Stopping Criteria.
1.4 Tensor Properties and Basis of Tensor Algebra.
1.4.1 Tensors (Multi-way Arrays) ? Preliminaries.
1.4.2 Subarrays, Tubes and Slices.
1.4.3 Unfolding ? Matricization.
1.4.4 Vectorization.
1.4.5 Outer, Kronecker, Khatri-Rao and Hadamard Products.
1.4.6 Mode-n Multiplication of Tensor by Matrix and
Tensor by Vector, Contracted Tensor Product.
1.4.7 Special Forms of Tensors.
1.5 Tensor Decompositions and Factorizations.
1.5.1 Why Multi-way Array Decompositions and Factorizations?
1.5.2 PARAFAC and Nonnegative Tensor Factorization.
1.5.3 NTF1 Model.
1.5.4 NTF2 Model.
1.5.5 Individual Differences in Scaling (INDSCAL) and Implicit
Slice Canonical Decomposition Model (IMCAND).
1.5.6 Shifted PARAFAC and Convolutive NTF.
1.5.7 Nonnegative Tucker Decompositions.
1.5.8 Block Component Decompositions.
1.5.9 Block-Oriented Decompositions.
1.5.10 PARATUCK2 and DEDICOM Models.
1.5.11 Hierarchical Tensor Decomposition.
1.6 Discussion and Conclusions.
2 Similarity Measures and Generalized Divergences.
2.1 Error-induced Distance and Robust Regression Techniques.
2.2 Robust Estimation.
2.3 Csiszar Divergences.
2.4 Bregman Divergence.
2.4.1 Bregman Matrix Divergences.
2.5 Alpha-Divergences.
2.5.1 Asymmetric Alpha-Divergences.
2.5.2 Symmetric Alpha-Divergences.
2.6 Beta-Divergences.
2.7 Gamma-Divergences.
2.8 Divergences Derived from Tsallis and Renyi Entropy.
2.8.1 Concluding Remarks.
3 Multiplicative Iterative Algorithms for NMF with Sparsity
Constraints.
3.1 Extended ISRA and EMML Algorithms: Regularization and
Sparsity.
3.1.1 Multiplicative NMF Algorithms Based on the Squared
Euclidean Distance.
3.1.2 Multiplicative NMF Algorithms Based on Kullback-Leibler
I-Divergence.
3.2 Multiplicative Algorithms Based on Alpha-Divergence.
3.2.1 Multiplicative Alpha NMF Algorithm.
3.2.2 Generalized Multiplicative Alpha NMF Algorithms.
3.3 Alternating SMART: Simultaneous Multiplicative Algebraic
Reconstruction Technique.
3.3.1 Alpha SMART Algorithm.
3.3.2 Generalized SMART Algorithms.
3.4 Multiplicative NMF Algorithms Based on Beta-Divergence.
3.4.1 Multiplicative Beta NMF Algorithm.
3.4.2 Multiplicative Algorithm Based on the Itakura-Saito
Distance.
3.4.3 Generalized Multiplicative Beta Algorithm for NMF.
3.5 Algorithms for Semi-orthogonal NMF and Orthogonal
Three-Factor NMF.
3.6 Multiplicative Algorithms for Affine NMF.
3.7 Multiplicative Algorithms for Convolutive NMF.
3.7.1 Multiplicative Algorithm for Convolutive NMF Based on
Alpha-Divergence.
3.7.2 Multiplicative Algorithm for Convolutive NMF Based on
Beta-Divergence.
3.7.3 Efficient Implementation of CNMF Algorithm.
3.8 Simulation Examples for Standard NMF.
3.9 Examples for Affine NMF.
3.10 Music Analysis and Decomposition Using Convolutive NMF.
3.11 Discussion and Conclusions.
4 Alternating Least Squares and Related Algorithms for NMF
and SCA Problems.
4.1 Standard ALS Algorithm.
4.1.1 Multiple Linear Regression ? Vectorized Version of
ALS Update Formulas.
4.1.2 Weighted ALS.
4.2 Methods for Improving Performance and Convergence Speed of
ALS Algorithms.
4.2.1 ALS Algorithm for Very Large-scale NMF.
4.2.2 ALS Algorithm with Line-Search.
4.2.3 Acceleration of ALS Algorithm via Simple
Regularization.
4.3 ALS Algorithm with Flexible and Generalized Regularization
Terms.
4.3.1 ALS with Tikhonov Type Regularization Terms.
4.3.2 ALS Algorithms with Sparsity Control and
Decorrelation.
4.4 Combined Generalized Regularized ALS Algorithms.
4.5 Wang-Hancewicz Modified ALS Algorithm.
4.6 Implementation of Regularized ALS Algorithms for NMF.
4.7 HALS Algorithm and its Extensions.
4.7.1 Projected Gradient Local Hierarchical Alternating Least
Squares (HALS) Algorithm.
4.7.2 Extensions and Implementations of the HALS Algorithm.
4.7.3 Fast HALS NMF Algorithm for Large-scale Problems.
4.7.4 HALS NMF Algorithm with Sparsity, Smoothness and
Uncorrelatedness Constraints.
4.7.5 HALS Algorithm for Sparse Component Analysis and Flexible
Component Analysis.
4.7.6 Simplified HALS Algorithm for Distributed and Multi-task
Compressed Sensing.
4.7.7 Generalized HALS-CS Algorithm.
4.7.8 Generalized HALS Algorithms Using Alpha-Divergence.
4.7.9 Generalized HALS Algorithms Using Beta-Divergence.
4.8 Simulation Results.
4.8.1 Underdetermined Blind Source Separation Examples.
4.8.2 NMF with Sparseness, Orthogonality and Smoothness
Constraints.
4.8.3 Simulations for Large-scale NMF.
4.8.4 Illustrative Examples for Compressed Sensing.
4.9 Discussion and Conclusions.
5 Projected Gradient Algorithms.
5.1 Oblique Projected Landweber (OPL) Method.
5.2 Lin?s Projected Gradient (LPG) Algorithm with Armijo
Rule.
5.3 Barzilai-Borwein Gradient Projection for Sparse
Reconstruction (GPSR-BB).
5.4 Projected Sequential Subspace Optimization (PSESOP).
5.5 Interior Point Gradient (IPG) Algorithm.
5.6 Interior Point Newton (IPN) Algorithm.
5.7 Regularized Minimal Residual Norm Steepest Descent Algorithm
(RMRNSD).
5.8 Sequential Coordinate-Wise Algorithm (SCWA).
5.9 Simulations.
5.10 Discussions.
6 Quasi-Newton Algorithms for Nonnegative Matrix
Factorization.
6.1 Projected Quasi-Newton Optimization.
6.1.1 Projected Quasi-Newton for Frobenius Norm.
6.1.2 Projected Quasi-Newton for Alpha-Divergence.
6.1.3 Projected Quasi-Newton for Beta-Divergence.
6.1.4 Practical Implementation.
6.2 Gradient Projection Conjugate Gradient.
6.3 FNMA algorithm.
6.4 NMF with Quadratic Programming.
6.4.1 Nonlinear Programming.
6.4.2 Quadratic Programming.
6.4.3 Trust-region Subproblem.
6.4.4 Updates for A.
6.5 Hybrid Updates.
6.6 Numerical Results.
6.7 Discussions.
7 Multi-Way Array (Tensor) Factorizations and
Decompositions.
7.1 Learning Rules for the Extended Three-way NTF1 Problem.
7.1.1 Basic Approaches for the Extended NTF1 Model.
7.1.2 ALS Algorithms for NTF1.
7.1.3 Multiplicative Alpha and Beta Algorithms for the NTF1
Model.
7.1.4 Multi-layer NTF1 Strategy.
7.2 Algorithms for Three-way Standard and Super Symmetric
Nonnegative Tensor Factorization.
7.2.1 Multiplicative NTF Algorithms Based on Alpha- and
Beta-Divergences.
7.2.2 Simple Alternative Approaches for NTF and SSNTF.
7.3 Nonnegative Tensor Factorizations for Higher-Order
Arrays.
7.3.1 Alpha NTF Algorithm.
7.3.2 Beta NTF Algorithm.
7.3.3 Fast HALS NTF Algorithm Using Squared Euclidean
Distance.
7.3.4 Generalized HALS NTF Algorithms Using Alpha- and
Beta-Divergences.
7.3.5 Tensor Factorization with Additional Constraints.
7.4 Algorithms for Nonnegative and Semi-Nonnegative Tucker
Decompositions.
7.4.1 Higher Order SVD (HOSVD) and Higher Order Orthogonal
Iteration (HOOI) Algorithms.
7.4.2 ALS Algorithm for Nonnegative Tucker Decomposition.
7.4.3 HOSVD, HOOI and ALS Algorithms as Initialization Tools for
Nonnegative Tensor Decomposition.
7.4.4 Multiplicative Alpha Algorithms for Nonnegative Tucker
Decomposition.
7.4.5 Beta NTD Algorithm.
7.4.6 Local ALS Algorithms for Nonnegative TUCKER
Decompositions.
7.4.7 Semi-Nonnegative Tucker Decomposition.
7.5 Nonnegative Block-Oriented Decomposition.
7.5.1 Multiplicative Algorithms for NBOD.
7.6 Multi-level Nonnegative Tensor Decomposition - High Accuracy
Compression and Approximation.
7.7 Simulations and Illustrative Examples.
7.7.1 Experiments for Nonnegative Tensor Factorizations.
7.7.2 Experiments for Nonnegative Tucker Decomposition.
7.7.3 Experiments for Nonnegative Block-Oriented
Decomposition.
7.7.4 Multi-Way Analysis of High Density Array EEG ?
Classification of Event Related Potentials.
7.7.5 Application of Tensor Decompositions in Brain Computer
Interface ? Classification of Motor Imagery Tasks.
7.7.6 Image and Video Applications.
7.8 Discussion and Conclusions.
8 Selected Applications.
8.1 Clustering.
8.1.1 Semi-Binary NMF.
8.1.2 NMF vs. Spectral Clustering.
8.1.3 Clustering with Convex NMF.
8.1.4 Application of NMF to Text Mining.
8.1.5 Email Surveillance.
8.2 Classification.
8.2.1 Musical Instrument Classification.
8.2.2 Image Classification.
8.3 Spectroscopy.
8.3.1 Raman Spectroscopy.
8.3.2 Fluorescence Spectroscopy.
8.3.3 Hyperspectral Imaging.
8.3.4 Chemical Shift Imaging.
8.4 Application of NMF for Analyzing Microarray Data.
8.4.1 Gene Expression Classification.
8.4.2 Analysis of Time Course Microarray Data.
References.
Index.
