This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF's various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). 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 Csiszár 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 Rényi 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.