mPadal: a joint local-and-global multi-view feature selection method for activity recognition

The selection of multi-view features plays an important role for classifying multi-view data, especially the data with high dimension. In this paper, a novel multi-view feature selection method via joint local pattern-discrimination and global label-relevance analysis (mPadal) is proposed. Different from the previous methods which globally select the multi-view features directly via view-level analysis, the proposed mPadal employs a new joint local-and-global way. In the local selection phase, the pattern-discriminative features will be first selected by considering the local neighbor structure of the most discriminative patterns. In the global selection phase, the features with the topmost label-relevance, which can well separate different classes in the current view, are selected. Finally, the two parts selected are combined to form the final features. Experimental results show that compared with several baseline methods in publicly available activity recognition dataset IXMAS, mPadal performs the best in terms of the highest accuracy, precision, recall and F1 score. Moreover, the features selected by mPadal are highly complementary among views for classification, which is able to improve the classification performance according to previous theoretical studies.