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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.
Produkteigenschaften
- Artikelnummer: 9781439806159
- Medium: Buch
- ISBN: 978-1-4398-0615-9
- Verlag: Taylor & Francis Inc
- Erscheinungstermin: 15.08.2011
- Sprache(n): Englisch
- Auflage: 1. Auflage 2011
- Serie: Chapman & Hall/CRC Machine Learning & Pattern Recognition
- Produktform: Gebunden
- Gewicht: 474 g
- Seiten: 208
- Format (B x H x T): 163 x 241 x 17 mm
- Ausgabetyp: Kein, Unbekannt