Open AccessMulti‐task Joint Feature Selection for Multi‐label Classification
Author(s) -
He Zhifen,
Yang Ming,
Liu Huidong
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.04.009
Subject(s) - multi label classification , computer science , artificial intelligence , pattern recognition (psychology) , feature selection , benchmark (surveying) , regularization (linguistics) , machine learning , optimization problem , convex optimization , feature vector , joint (building) , regular polygon , algorithm , mathematics , architectural engineering , engineering , geometry , geodesy , geography
Multi‐label learning deals with each instance which may be associated with a set of class labels simultaneously. We propose a novel multi‐label classification approach named MFSM (Multi‐task joint feature selection for multi‐label classification). In MFSM, we compute the asymmetric label correlation matrix in the label space. The multi‐label learning problem can be formulated as a joint optimization problem including two regularization terms, one aims to consider the label correlations and the other is used to select the similar sparse features shared among multiple different classification tasks (each for one label). Our model can be reformulated into an equivalent smooth convex optimization problem which can be solved by the Nesterov's method. The experiments on sixteen benchmark multi‐label data sets demonstrate that our method outperforms the state‐of‐the‐art multi‐label learning algorithms.