Opis: Multi-label Dimensionality Reduction - Liang Sun, Shuiwang Ji, Jieping Ye

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(R) package for implementing popular dimensionality reduction algorithms.Introduction Introduction to Multi-Label Learning Applications of Multi-Label Learning Challenges of Multi-Label Learning State of the Art Dimensionality Reduction for Multi-Label Learning Overview of the Book Notations Organization Partial Least Squares Basic Models of Partial Least Squares Partial Least Squares Variants Partial Least Squares Regression Partial Least Squares Classification Canonical Correlation Analysis Classical Canonical Correlation Sparse CCA Relationship between CCA and Partial Least Squares The Generalized Eigenvalue Problem Hypergraph Spectral Learning Hypergraph Basics Multi-Label Learning with a Hypergraph A Class of Generalized Eigenvalue Problems The Generalized Eigenvalue Problem versus the Least Squares Problem Empirical Evaluation A Scalable Two-Stage Approach for Dimensionality Reduction The Two-Stage Approach with Regularization Empirical Evaluation A Shared-Subspace Learning Framework The Framework An Efficient Implementation Related Work Connections with Existing Formulations A Feature Space Formulation Empirical Evaluation Joint Dimensionality Reduction and Classification Background Joint Dimensionality Reduction and Multi-Label Classification Dimensionality Reduction with Different Input Data Empirical Evaluation Nonlinear Dimensionality Reduction: Algorithms and Applications Background on Kernel Methods Kernel Centering and Projection Kernel Canonical Correlation Analysis Kernel Hypergraph Spectral Learning The Generalized Eigenvalue Problem in the Kernel-Induced Feature Space Kernel Least Squares Regression Dimensionality Reduction and Least Squares Regression in the Feature Space Gene Expression Pattern Image Annotation Appendix: Proofs References Index

Szczegóły: Multi-label Dimensionality Reduction - Liang Sun, Shuiwang Ji, Jieping Ye

Tytuł: Multi-label Dimensionality Reduction
Autor: Liang Sun, Shuiwang Ji, Jieping Ye
Producent: Chapman
ISBN: 9781439806159
Rok produkcji: 2011
Ilość stron: 208
Oprawa: Twarda
Waga: 0.47 kg

Recenzje: Multi-label Dimensionality Reduction - Liang Sun, Shuiwang Ji, Jieping Ye