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Record Nr. |
UNINA9910789003903321 |
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Autore |
Lu Haiping |
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Titolo |
Multilinear subspace learning : dimensionality reduction of multidimensional data / / Haiping Lu, K. N. Plataniotis, A. N. Venetsanopoulos |
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Pubbl/distr/stampa |
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Boca Raton, Florida : , : CRC Press, , 2014 |
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©2014 |
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ISBN |
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0-429-10809-5 |
1-4398-5729-6 |
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Descrizione fisica |
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1 online resource (275 p.) |
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Collana |
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Chapman & Hall/CRC machine learning & pattern recognition series Multilinear subspace learning |
Chapman & Hall/CRC machine learning & pattern recognition series |
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Classificazione |
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COM021030COM037000TEC007000 |
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Altri autori (Persone) |
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PlataniotisK. N |
VenetsanopoulosA. N |
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Disciplina |
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Soggetti |
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Data compression (Computer science) |
Big data |
Multilinear algebra |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Front Cover; Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data; Copyright; Dedication; Table of Contents; List of Figures; List of Tables; List of Algorithms; Acronyms and Symbols; Preface; 1. Introduction; Part I: Fundamentals and Foundations; 2. Linear Subspace Learning for Dimensionality Reduction; 3. Fundamentals of Multilinear Subspace Learning; 4. Overview of Multilinear Subspace Learning; 5. Algorithmic and Computational Aspects; Part II: Algorithms and Applications; 6. Multilinear Principal Component Analysis; 7. Multilinear Discriminant Analysis |
8. Multilinear ICA, CCA, and PLS9. Applications of Multilinear Subspace Learning; Appendix A: Mathematical Background; Appendix B: Data and Preprocessing; Appendix C: Software; Bibliography; Back Cover |
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Sommario/riassunto |
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Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and |
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