portada Multi-Label Dimensionality Reduction (en Inglés)

Multi-Label Dimensionality Reduction (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

208

Encuadernación

Tapa Dura

Dimensiones

23.6x15.2x1.5 cm

Peso

0.64 kg.

ISBN13

9781439806159

Categorías

Econometría
Desarrollo y programación de juegos
Bases de datos

Multi-Label Dimensionality Reduction (en Inglés)

Liang Sun (Autor) · Shuiwang Ji (Autor) · Jieping Ye (Autor) · CRC Press · Tapa Dura

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Reseña del libro "Multi-Label Dimensionality Reduction (en Inglés)"

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.