Multi-label Linear Discriminant Analysis with Locality Consistency

Yuzhang Yuan, Kang Zhao, Hongtao Lu

Abstract: Multi-label classification is common in many domains such as text categorization, automatic multimedia annotation and bioinformatics, etc. Multi-label linear discriminant analysis (MLDA) is an available algorithm for solving multi-label problems, which captures the global structure by employing the forceful classification ability of the classical linear discriminant analysis. However, some latest studies prove that local geometric structure is crucial for classification. In this paper, we present a new method called Multi-label Linear Discriminant Analysis with Locality Consistency (MLDA-LC) which incorporates local structure into the framework of MLDA. Specifically, we employ a graph regularized term to preserve the local structure for multi-label data. In addition, an efficient computing method is also presented to reduce the time and space cost of computation. The experimental results on three benchmark multi-label data sets demonstrate that our algorithm is feasible and effective.