Multi-view adaptive semi-supervised feature selection with the self-paced learning

Abstract

In recent decades, semi-supervised feature selection (SSFS) has gained extensive research in the field of machine learning and computer vision. Most SSFS algorithms are based on graph-based semi-supervised learning (GSSL), and their performance depend heavily on the quality of the Laplacian weight graph. However, the Laplacian weight graph can't be changed once it is constructed, which greatly restricts the performance of SSFS. To address this defect, in this paper we propose a novel Multi-view Adaptive Semi-supervised Feature Selection (MASFS) algorithm, which introduces the self-paced learning (SPL) into SSFS to make the Laplacian weight graph adaptively change according to the current predicted information. Meanwhile, the MASFS algorithm utilizes the multi-view learning to effectively explore the complementary and related information contained in different views to enhance SSFS performance. We propose a valid iterative algorithm for optimizing the objective function, followed by the convergence analysis and the complexity analysis. To illustrate the effectiveness of the MASFS algorithm, some experiments are carried out on NUS-WIDE dataset and MSRA-MM2.0 dataset and the experimental results indicate that MASFS has better performance than other SSFS algorithms based on GSSL.