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chapman & hall/crc machine learning & pattern recognition series multi-label dimensionality reduction,liang sun,shuiwang ji 🔍
Sun, Liang ;Ji, Shuiwang ;Ye, Jieping Chapman and Hall/CRC, Chapman & Hall/CRC Machine Learning & Pattern Recognition, 0, 2016 apr 19
英语 [en] · PDF · 3.3MB · 2016 · 📘 非小说类图书 · 🚀/duxiu/lgli/lgrs/nexusstc/upload/zlib · Save
描述
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications.
Addressing this shortfall, **Multi-Label Dimensionality Reduction** covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including:
* __How to fully exploit label correlations for effective dimensionality reduction__
* __How to scale dimensionality reduction algorithms to large-scale problems__
* __How to effectively combine dimensionality reduction with classification__
* __How to derive sparse dimensionality reduction algorithms to enhance model interpretability__
* __How to perform multi-label dimensionality reduction effectively in practical applications__
The authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of __Drosophila__ gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB® package for implementing popular dimensionality reduction algorithms.
备用文件名
lgli/G:\!genesis\_add\CRC\Multi-Label Dimensionality Reduction - Sun Liang & Ji Shuiwang & Ye Jieping.pdf
备用文件名
lgrsnf/G:\!genesis\_add\CRC\Multi-Label Dimensionality Reduction - Sun Liang & Ji Shuiwang & Ye Jieping.pdf
备用文件名
nexusstc/Multi-Label Dimensionality Reduction/9880d6720c107975994348750f4e2495.pdf
备用文件名
zlib/Mathematics/Liang Sun, Shuiwang Ji, Jieping Ye/Multi-Label Dimensionality Reduction_2374061.pdf
备选标题
Multi-Label Dimensionality Reduction (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
备选作者
Liang Sun.; Shuiwang Ji; Jieping Ye
备选作者
and jieping ye
备用出版商
Taylor & Francis Group
备用出版商
Taylor & Francis Ltd
备用出版商
CRC Press LLC
备用版本
Chapman & Hall/CRC machine learning & pattern recognition series, Boca Raton, Florida, 2014
备用版本
Chapman & Hall/CRC machine learning & pattern recognition series, Boca Raton, FL, 2013
备用版本
Chapman & Hall/CRC Machine Learning & Pattern Recognition, 0, 2013
备用版本
CRC Press (Unlimited), Boca Raton, FL, 2014
备用版本
United Kingdom and Ireland, United Kingdom
备用版本
United States, United States of America
备用版本
1st, 2016
备用版本
1, 2013
备用版本
2012
元数据中的注释
lg1205754
元数据中的注释
producers:
GPL Ghostscript 9.10
元数据中的注释
{"isbns":["0429148208","1439806152","1439806160","9780429148200","9781439806159","9781439806166"],"last_page":208,"publisher":"Chapman and Hall/CRC","series":"Chapman & Hall/CRC Machine Learning & Pattern Recognition"}
备用描述
Cover 1
Series 3
Contents 6
Preface 12
Symbol Description 14
Chapter 1: Introduction 16
Chapter 2: Partial Least Squares 44
Chapter 3: Canonical Correlation Analysis 64
Chapter 4: Hypergraph Spectral Learning 84
Chapter 5: A Scalable Two-Stage Approach for Dimensionality Reduction 106
Chapter 6: A Shared-Subspace Learning Framework 120
Chapter 7: Joint Dimensionality Reduction and Classification 138
Chapter 8: Nonlinear Dimensionality Reduction: Algorithms and Applications 148
Appendix Proofs 170
References 182
Back Cover 206
备用描述
Suitable for researchers in machine learning, data mining, and computer vision, this book presents discussions on algorithms and applications for dimensionality reduction. It covers models for general dimensionality reduction in multi-label classification. It also presents a novel framework to unify a variety of models.
开源日期
2014-08-31
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