Multi-Label Dimensionality Reduction Hardback
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 reductionHow to scale dimensionality reduction algorithms to large-scale problemsHow to effectively combine dimensionality reduction with classificationHow to derive sparse dimensionality reduction algorithms to enhance model interpretabilityHow to perform multi-label dimensionality reduction effectively in practical applicationsThe 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.
- Format: Hardback
- Pages: 208 pages, 14 Tables, black and white; 23 Illustrations, black and white
- Publisher: Taylor & Francis Ltd
- Publication Date: 15/08/2011
- Category: Data mining
- ISBN: 9781439806159