Multi-Label Dimensionality Reduction

<p>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. </p><p>Addressing this shortfall <strong>Multi-Label Dimensionality Reduction</strong> covers the methodological developments theoretical properties computational aspects and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions including: </p><ul> <p> </p> <li><em>How to fully exploit label correlations for effective dimensionality reduction</em></li> <li><em>How to scale dimensionality reduction algorithms to large-scale problems</em></li> <li><em>How to effectively combine dimensionality reduction with classification</em></li> <li><em>How to derive sparse dimensionality reduction algorithms to enhance model interpretability</em></li> <li><em>How to perform multi-label dimensionality reduction effectively in practical applications</em></li> </ul><p>The authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of <em>Drosophila</em> 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.</p>