Unsupervised multi-view feature selection based on weighted low-rank tensor learning and its application in multi-omics datasets

Daoyuan Wang, Lianzhi Wang, Wenlan Chen, Hong Wang, Cheng Liang

Abstract: With the explosive growth of unlabeled multi-view data with high dimensionality in various fields such as bioinformatics, unsupervised multi-view feature selection has become a key technique to simultaneously handle the curse of dimensionality in multiple datasets. Most existing methods heavily rely on the pseudo labels obtained from respective views, where they perform feature selections without comprehensively exploiting the high-order connections among views. In this paper, we propose a weighted low-rank tensor-based unsupervised multi-view feature selection framework (WLTL), which integrates multi-view spectral clustering and weighted low-rank tensor to generate high-quality pseudo labels for feature selection. Specifically, we stack the clustering indicator matrices into a three-dimensional tensor and impose the weighted tensor nuclear norm constraint, which captures high-order correlations and consistent pseudo label information while allowing for more refined consideration of the importance of each view. Our method also features an adaptive strategy to automatically assign view weights, optimizing the feature selection process by considering both individual view characteristics and inter-view relationships. We present an efficient optimization algorithm for iteratively refining the proposed framework. Extensive experiments implemented on six machine learning and three multi-omics datasets confirm the superiority of WLTL. Additionally, the case study performed on the cancer dataset serves to affirm the practicality of our model.