Multilinear subspace learning : (Record no. 16157)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03566nam a2200469Ii 4500 |
| 001 - CONTROL NUMBER | |
| control field | CAH0KE12717PDF |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FlBoTFG |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20171224124002.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION | |
| fixed length control field | m|||||o||d|||||||| |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr|||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150323t20142014flua ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781439857298 (e-book : PDF) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FlBoTFG |
| Language of cataloging | eng |
| Transcribing agency | FlBoTFG |
| Description conventions | rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (OCLC) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | QA76.9.D33 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) | L825 2013 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 005.7 |
| Item number | L926 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lu, Haiping, |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Multilinear subspace learning : |
| Remainder of title | dimensionality reduction of multidimensional data / |
| Statement of responsibility, etc | Haiping Lu, K.N. Plataniotis, A.N. Venetsanopoulos. |
| 264 #1 - | |
| -- | Boca Raton : |
| -- | Taylor & Francis / CRC, |
| -- | [2014] |
| 264 #4 - | |
| -- | �201 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource : |
| Other physical details | text file, PD |
| 336 ## - | |
| -- | text |
| -- | rdaconten |
| 337 ## - | |
| -- | computer |
| -- | rdamedi |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrie |
| 490 ## - SERIES STATEMENT | |
| Series statement | Chapman & Hall/CRC machine learning & pattern recognition serie |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references (pages 231-261) and index |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1. Fundamentals and foundations -- 2. Algorithms and applications |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "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 medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear subspace learning : dimensionality reduction of multidimensional data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today's most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications.The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB source code, data, and other materials are available at www.comp.hkbu.edu.hk/~haiping/MSL.html"-- |
| -- | Provided by publisher |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Additional physical form available note | Also available in print format |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data compression (Computer science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Big data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Multilinear algebra |
| 655 ## - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| Source of term | lcs |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Plataniotis, Konstantinos N., |
| Relator term | author |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Venetsanopoulos, A. N. |
| Fuller form of name | (Anastasios N.), |
| Dates associated with a name | 1941- |
| Relator term | author |
| 776 ## - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Print version: |
| International Standard Book Number | 978143985724 |
| 830 ## - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Chapman & Hall/CRC machine learning & pattern recognition series |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://marc.crcnetbase.com/isbn/9781439857298">http://marc.crcnetbase.com/isbn/9781439857298</a> |
| Electronic format type | application/PDF |
| Public note | Distributed by publisher. Purchase or institutional license may be required for access |
| 913 ## - | |
| -- | 12594 |
| 993 ## - | |
| -- | CAH0KE12717PD |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| -- | existing prin |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| -- | xxvii, 268 pages : |
| Operator's initials, OID (RLIN) | illustrations ; |
| Cataloger's initials, CIN (RLIN) | [ca. 23-29] cm |
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