{"product_id":"nonnegative-matrix-and-tensor-factorizations-applications-to-exploratory-multiway-data-analysis-and-blind-source-separation-9780470746660","title":"Nonnegative Matrix and Tensor Factorizations","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis 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).\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"[A] focus on the algorithms that are most useful in practice and aim to derive and implement, in MATLAB, efficient and simple iterative algorithms that work with real-world data.\" (\u003ci\u003eBook News\u003c\/i\u003e, December 2009)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eGlossary of Symbols and Abbreviations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction – Problem Statements and Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Blind Source Separation and Linear Generalized Component Analysis.\u003c\/p\u003e \u003cp\u003e1.2 Matrix Factorization Models with Nonnegativity and Sparsity Constraints.\u003c\/p\u003e \u003cp\u003e1.2.1 Why Nonnegativity and Sparsity Constraints?\u003c\/p\u003e \u003cp\u003e1.2.2 Basic NMF Model.\u003c\/p\u003e \u003cp\u003e1.2.3 Symmetric NMF.\u003c\/p\u003e \u003cp\u003e1.2.4 Semi-Orthogonal NMF.\u003c\/p\u003e \u003cp\u003e1.2.5 Semi-NMF and Nonnegative Factorization of Arbitrary Matrix.\u003c\/p\u003e \u003cp\u003e1.2.6 Three-factor NMF.\u003c\/p\u003e \u003cp\u003e1.2.7 NMF with Offset (Affine NMF).\u003c\/p\u003e \u003cp\u003e1.2.8 Multi-layer NMF.\u003c\/p\u003e \u003cp\u003e1.2.9 Simultaneous NMF.\u003c\/p\u003e \u003cp\u003e1.2.10 Projective and Convex NMF.\u003c\/p\u003e \u003cp\u003e1.2.11 Kernel NMF.\u003c\/p\u003e \u003cp\u003e1.2.12 Convolutive NMF.\u003c\/p\u003e \u003cp\u003e1.2.13 Overlapping NMF.\u003c\/p\u003e \u003cp\u003e1.3 Basic Approaches to Estimate Parameters of Standard NMF.\u003c\/p\u003e \u003cp\u003e1.3.1 Large-scale NMF.\u003c\/p\u003e \u003cp\u003e1.3.2 Non-uniqueness of NMF and Techniques to Alleviate the Ambiguity Problem.\u003c\/p\u003e \u003cp\u003e1.3.3 Initialization of NMF.\u003c\/p\u003e \u003cp\u003e1.3.4 Stopping Criteria.\u003c\/p\u003e \u003cp\u003e1.4 Tensor Properties and Basis of Tensor Algebra.\u003c\/p\u003e \u003cp\u003e1.4.1 Tensors (Multi-way Arrays) – Preliminaries.\u003c\/p\u003e \u003cp\u003e1.4.2 Subarrays, Tubes and Slices.\u003c\/p\u003e \u003cp\u003e1.4.3 Unfolding – Matricization.\u003c\/p\u003e \u003cp\u003e1.4.4 Vectorization.\u003c\/p\u003e \u003cp\u003e1.4.5 Outer, Kronecker, Khatri-Rao and Hadamard Products.\u003c\/p\u003e \u003cp\u003e1.4.6 Mode-\u003ci\u003en\u003c\/i\u003e Multiplication of Tensor by Matrix and Tensor by Vector, Contracted Tensor Product.\u003c\/p\u003e \u003cp\u003e1.4.7 Special Forms of Tensors.\u003c\/p\u003e \u003cp\u003e1.5 Tensor Decompositions and Factorizations.\u003c\/p\u003e \u003cp\u003e1.5.1 Why Multi-way Array Decompositions and Factorizations?\u003c\/p\u003e \u003cp\u003e1.5.2 PARAFAC and Nonnegative Tensor Factorization.\u003c\/p\u003e \u003cp\u003e1.5.3 NTF1 Model.\u003c\/p\u003e \u003cp\u003e1.5.4 NTF2 Model.\u003c\/p\u003e \u003cp\u003e1.5.5 Individual Differences in Scaling (INDSCAL) and Implicit Slice Canonical Decomposition Model (IMCAND).\u003c\/p\u003e \u003cp\u003e1.5.6 Shifted PARAFAC and Convolutive NTF.\u003c\/p\u003e \u003cp\u003e1.5.7 Nonnegative Tucker Decompositions.\u003c\/p\u003e \u003cp\u003e1.5.8 Block Component Decompositions.\u003c\/p\u003e \u003cp\u003e1.5.9 Block-Oriented Decompositions.\u003c\/p\u003e \u003cp\u003e1.5.10 PARATUCK2 and DEDICOM Models.\u003c\/p\u003e \u003cp\u003e1.5.11 Hierarchical Tensor Decomposition.\u003c\/p\u003e \u003cp\u003e1.6 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Similarity Measures and Generalized Divergences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Error-induced Distance and Robust Regression Techniques.\u003c\/p\u003e \u003cp\u003e2.2 Robust Estimation.\u003c\/p\u003e \u003cp\u003e2.3 Csiszár Divergences.\u003c\/p\u003e \u003cp\u003e2.4 Bregman Divergence.\u003c\/p\u003e \u003cp\u003e2.4.1 Bregman Matrix Divergences.\u003c\/p\u003e \u003cp\u003e2.5 Alpha-Divergences.\u003c\/p\u003e \u003cp\u003e2.5.1 Asymmetric Alpha-Divergences.\u003c\/p\u003e \u003cp\u003e2.5.2 Symmetric Alpha-Divergences.\u003c\/p\u003e \u003cp\u003e2.6 Beta-Divergences.\u003c\/p\u003e \u003cp\u003e2.7 Gamma-Divergences.\u003c\/p\u003e \u003cp\u003e2.8 Divergences Derived from Tsallis and Rényi Entropy.\u003c\/p\u003e \u003cp\u003e2.8.1 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multiplicative Iterative Algorithms for NMF with Sparsity Constraints.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Extended ISRA and EMML Algorithms: Regularization and Sparsity.\u003c\/p\u003e \u003cp\u003e3.1.1 Multiplicative NMF Algorithms Based on the Squared Euclidean Distance.\u003c\/p\u003e \u003cp\u003e3.1.2 Multiplicative NMF Algorithms Based on Kullback-Leibler I-Divergence.\u003c\/p\u003e \u003cp\u003e3.2 Multiplicative Algorithms Based on Alpha-Divergence.\u003c\/p\u003e \u003cp\u003e3.2.1 Multiplicative Alpha NMF Algorithm.\u003c\/p\u003e \u003cp\u003e3.2.2 Generalized Multiplicative Alpha NMF Algorithms.\u003c\/p\u003e \u003cp\u003e3.3 Alternating SMART: Simultaneous Multiplicative Algebraic Reconstruction Technique.\u003c\/p\u003e \u003cp\u003e3.3.1 Alpha SMART Algorithm.\u003c\/p\u003e \u003cp\u003e3.3.2 Generalized SMART Algorithms.\u003c\/p\u003e \u003cp\u003e3.4 Multiplicative NMF Algorithms Based on Beta-Divergence.\u003c\/p\u003e \u003cp\u003e3.4.1 Multiplicative Beta NMF Algorithm.\u003c\/p\u003e \u003cp\u003e3.4.2 Multiplicative Algorithm Based on the Itakura-Saito Distance.\u003c\/p\u003e \u003cp\u003e3.4.3 Generalized Multiplicative Beta Algorithm for NMF.\u003c\/p\u003e \u003cp\u003e3.5 Algorithms for Semi-orthogonal NMF and Orthogonal Three-Factor NMF.\u003c\/p\u003e \u003cp\u003e3.6 Multiplicative Algorithms for Affine NMF.\u003c\/p\u003e \u003cp\u003e3.7 Multiplicative Algorithms for Convolutive NMF.\u003c\/p\u003e \u003cp\u003e3.7.1 Multiplicative Algorithm for Convolutive NMF Based on Alpha-Divergence.\u003c\/p\u003e \u003cp\u003e3.7.2 Multiplicative Algorithm for Convolutive NMF Based on Beta-Divergence.\u003c\/p\u003e \u003cp\u003e3.7.3 Efficient Implementation of CNMF Algorithm.\u003c\/p\u003e \u003cp\u003e3.8 Simulation Examples for Standard NMF.\u003c\/p\u003e \u003cp\u003e3.9 Examples for Affine NMF.\u003c\/p\u003e \u003cp\u003e3.10 Music Analysis and Decomposition Using Convolutive NMF.\u003c\/p\u003e \u003cp\u003e3.11 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Alternating Least Squares and Related Algorithms for NMF and SCA Problems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Standard ALS Algorithm.\u003c\/p\u003e \u003cp\u003e4.1.1 Multiple Linear Regression – Vectorized Version of ALS Update Formulas.\u003c\/p\u003e \u003cp\u003e4.1.2 Weighted ALS.\u003c\/p\u003e \u003cp\u003e4.2 Methods for Improving Performance and Convergence Speed of ALS Algorithms.\u003c\/p\u003e \u003cp\u003e4.2.1 ALS Algorithm for Very Large-scale NMF.\u003c\/p\u003e \u003cp\u003e4.2.2 ALS Algorithm with Line-Search.\u003c\/p\u003e \u003cp\u003e4.2.3 Acceleration of ALS Algorithm via Simple Regularization.\u003c\/p\u003e \u003cp\u003e4.3 ALS Algorithm with Flexible and Generalized Regularization Terms.\u003c\/p\u003e \u003cp\u003e4.3.1 ALS with Tikhonov Type Regularization Terms.\u003c\/p\u003e \u003cp\u003e4.3.2 ALS Algorithms with Sparsity Control and Decorrelation.\u003c\/p\u003e \u003cp\u003e4.4 Combined Generalized Regularized ALS Algorithms.\u003c\/p\u003e \u003cp\u003e4.5 Wang-Hancewicz Modified ALS Algorithm.\u003c\/p\u003e \u003cp\u003e4.6 Implementation of Regularized ALS Algorithms for NMF.\u003c\/p\u003e \u003cp\u003e4.7 HALS Algorithm and its Extensions.\u003c\/p\u003e \u003cp\u003e4.7.1 Projected Gradient Local Hierarchical Alternating Least Squares (HALS) Algorithm.\u003c\/p\u003e \u003cp\u003e4.7.2 Extensions and Implementations of the HALS Algorithm.\u003c\/p\u003e \u003cp\u003e4.7.3 Fast HALS NMF Algorithm for Large-scale Problems.\u003c\/p\u003e \u003cp\u003e4.7.4 HALS NMF Algorithm with Sparsity, Smoothness and Uncorrelatedness Constraints.\u003c\/p\u003e \u003cp\u003e4.7.5 HALS Algorithm for Sparse Component Analysis and Flexible Component Analysis.\u003c\/p\u003e \u003cp\u003e4.7.6 Simplified HALS Algorithm for Distributed and Multi-task Compressed Sensing.\u003c\/p\u003e \u003cp\u003e4.7.7 Generalized HALS-CS Algorithm.\u003c\/p\u003e \u003cp\u003e4.7.8 Generalized HALS Algorithms Using Alpha-Divergence.\u003c\/p\u003e \u003cp\u003e4.7.9 Generalized HALS Algorithms Using Beta-Divergence.\u003c\/p\u003e \u003cp\u003e4.8 Simulation Results.\u003c\/p\u003e \u003cp\u003e4.8.1 Underdetermined Blind Source Separation Examples.\u003c\/p\u003e \u003cp\u003e4.8.2 NMF with Sparseness, Orthogonality and Smoothness Constraints.\u003c\/p\u003e \u003cp\u003e4.8.3 Simulations for Large-scale NMF.\u003c\/p\u003e \u003cp\u003e4.8.4 Illustrative Examples for Compressed Sensing.\u003c\/p\u003e \u003cp\u003e4.9 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Projected Gradient Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Oblique Projected Landweber (OPL) Method.\u003c\/p\u003e \u003cp\u003e5.2 Lin’s Projected Gradient (LPG) Algorithm with Armijo Rule.\u003c\/p\u003e \u003cp\u003e5.3 Barzilai-Borwein Gradient Projection for Sparse Reconstruction (GPSR-BB).\u003c\/p\u003e \u003cp\u003e5.4 Projected Sequential Subspace Optimization (PSESOP).\u003c\/p\u003e \u003cp\u003e5.5 Interior Point Gradient (IPG) Algorithm.\u003c\/p\u003e \u003cp\u003e5.6 Interior Point Newton (IPN) Algorithm.\u003c\/p\u003e \u003cp\u003e5.7 Regularized Minimal Residual Norm Steepest Descent Algorithm (RMRNSD).\u003c\/p\u003e \u003cp\u003e5.8 Sequential Coordinate-Wise Algorithm (SCWA).\u003c\/p\u003e \u003cp\u003e5.9 Simulations.\u003c\/p\u003e \u003cp\u003e5.10 Discussions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Quasi-Newton Algorithms for Nonnegative Matrix Factorization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Projected Quasi-Newton Optimization.\u003c\/p\u003e \u003cp\u003e6.1.1 Projected Quasi-Newton for Frobenius Norm.\u003c\/p\u003e \u003cp\u003e6.1.2 Projected Quasi-Newton for Alpha-Divergence.\u003c\/p\u003e \u003cp\u003e6.1.3 Projected Quasi-Newton for Beta-Divergence.\u003c\/p\u003e \u003cp\u003e6.1.4 Practical Implementation.\u003c\/p\u003e \u003cp\u003e6.2 Gradient Projection Conjugate Gradient.\u003c\/p\u003e \u003cp\u003e6.3 FNMA algorithm.\u003c\/p\u003e \u003cp\u003e6.4 NMF with Quadratic Programming.\u003c\/p\u003e \u003cp\u003e6.4.1 Nonlinear Programming.\u003c\/p\u003e \u003cp\u003e6.4.2 Quadratic Programming.\u003c\/p\u003e \u003cp\u003e6.4.3 Trust-region Subproblem.\u003c\/p\u003e \u003cp\u003e6.4.4 Updates for A.\u003c\/p\u003e \u003cp\u003e6.5 Hybrid Updates.\u003c\/p\u003e \u003cp\u003e6.6 Numerical Results.\u003c\/p\u003e \u003cp\u003e6.7 Discussions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multi-Way Array (Tensor) Factorizations and Decompositions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Learning Rules for the Extended Three-way NTF1 Problem.\u003c\/p\u003e \u003cp\u003e7.1.1 Basic Approaches for the Extended NTF1 Model.\u003c\/p\u003e \u003cp\u003e7.1.2 ALS Algorithms for NTF1.\u003c\/p\u003e \u003cp\u003e7.1.3 Multiplicative Alpha and Beta Algorithms for the NTF1 Model.\u003c\/p\u003e \u003cp\u003e7.1.4 Multi-layer NTF1 Strategy.\u003c\/p\u003e \u003cp\u003e7.2 Algorithms for Three-way Standard and Super Symmetric Nonnegative Tensor Factorization.\u003c\/p\u003e \u003cp\u003e7.2.1 Multiplicative NTF Algorithms Based on Alpha- and Beta-Divergences.\u003c\/p\u003e \u003cp\u003e7.2.2 Simple Alternative Approaches for NTF and SSNTF.\u003c\/p\u003e \u003cp\u003e7.3 Nonnegative Tensor Factorizations for Higher-Order Arrays.\u003c\/p\u003e \u003cp\u003e7.3.1 Alpha NTF Algorithm.\u003c\/p\u003e \u003cp\u003e7.3.2 Beta NTF Algorithm.\u003c\/p\u003e \u003cp\u003e7.3.3 Fast HALS NTF Algorithm Using Squared Euclidean Distance.\u003c\/p\u003e \u003cp\u003e7.3.4 Generalized HALS NTF Algorithms Using Alpha- and Beta-Divergences.\u003c\/p\u003e \u003cp\u003e7.3.5 Tensor Factorization with Additional Constraints.\u003c\/p\u003e \u003cp\u003e7.4 Algorithms for Nonnegative and Semi-Nonnegative Tucker Decompositions.\u003c\/p\u003e \u003cp\u003e7.4.1 Higher Order SVD (HOSVD) and Higher Order Orthogonal Iteration (HOOI) Algorithms.\u003c\/p\u003e \u003cp\u003e7.4.2 ALS Algorithm for Nonnegative Tucker Decomposition.\u003c\/p\u003e \u003cp\u003e7.4.3 HOSVD, HOOI and ALS Algorithms as Initialization Tools for Nonnegative Tensor Decomposition.\u003c\/p\u003e \u003cp\u003e7.4.4 Multiplicative Alpha Algorithms for Nonnegative Tucker Decomposition.\u003c\/p\u003e \u003cp\u003e7.4.5 Beta NTD Algorithm.\u003c\/p\u003e \u003cp\u003e7.4.6 Local ALS Algorithms for Nonnegative TUCKER Decompositions.\u003c\/p\u003e \u003cp\u003e7.4.7 Semi-Nonnegative Tucker Decomposition.\u003c\/p\u003e \u003cp\u003e7.5 Nonnegative Block-Oriented Decomposition.\u003c\/p\u003e \u003cp\u003e7.5.1 Multiplicative Algorithms for NBOD.\u003c\/p\u003e \u003cp\u003e7.6 Multi-level Nonnegative Tensor Decomposition - High Accuracy Compression and Approximation.\u003c\/p\u003e \u003cp\u003e7.7 Simulations and Illustrative Examples.\u003c\/p\u003e \u003cp\u003e7.7.1 Experiments for Nonnegative Tensor Factorizations.\u003c\/p\u003e \u003cp\u003e7.7.2 Experiments for Nonnegative Tucker Decomposition.\u003c\/p\u003e \u003cp\u003e7.7.3 Experiments for Nonnegative Block-Oriented Decomposition.\u003c\/p\u003e \u003cp\u003e7.7.4 Multi-Way Analysis of High Density Array EEG – Classification of Event Related Potentials.\u003c\/p\u003e \u003cp\u003e7.7.5 Application of Tensor Decompositions in Brain Computer Interface – Classification of Motor Imagery Tasks.\u003c\/p\u003e \u003cp\u003e7.7.6 Image and Video Applications.\u003c\/p\u003e \u003cp\u003e7.8 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Selected Applications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Clustering.\u003c\/p\u003e \u003cp\u003e8.1.1 Semi-Binary NMF.\u003c\/p\u003e \u003cp\u003e8.1.2 NMF vs. Spectral Clustering.\u003c\/p\u003e \u003cp\u003e8.1.3 Clustering with Convex NMF.\u003c\/p\u003e \u003cp\u003e8.1.4 Application of NMF to Text Mining.\u003c\/p\u003e \u003cp\u003e8.1.5 Email Surveillance.\u003c\/p\u003e \u003cp\u003e8.2 Classification.\u003c\/p\u003e \u003cp\u003e8.2.1 Musical Instrument Classification.\u003c\/p\u003e \u003cp\u003e8.2.2 Image Classification.\u003c\/p\u003e \u003cp\u003e8.3 Spectroscopy.\u003c\/p\u003e \u003cp\u003e8.3.1 Raman Spectroscopy.\u003c\/p\u003e \u003cp\u003e8.3.2 Fluorescence Spectroscopy.\u003c\/p\u003e \u003cp\u003e8.3.3 Hyperspectral Imaging.\u003c\/p\u003e \u003cp\u003e8.3.4 Chemical Shift Imaging.\u003c\/p\u003e \u003cp\u003e8.4 Application of NMF for Analyzing Microarray Data.\u003c\/p\u003e \u003cp\u003e8.4.1 Gene Expression Classification.\u003c\/p\u003e \u003cp\u003e8.4.2 Analysis of Time Course Microarray Data.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402424721751,"sku":"9780470746660","price":107.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470746660.jpg?v=1730480360","url":"https:\/\/bookcurl.com\/products\/nonnegative-matrix-and-tensor-factorizations-applications-to-exploratory-multiway-data-analysis-and-blind-source-separation-9780470746660","provider":"Book Curl","version":"1.0","type":"link"}