TRUST-FS: Tensorized Reliable Unsupervised Multi-View Feature Selection for Incomplete Data
This addresses a practical limitation in machine learning for multi-view data analysis where existing methods cannot handle missing variables, though it appears incremental as it builds on tensor factorization and similarity graph techniques.
The paper tackles the problem of multi-view unsupervised feature selection with incomplete data where features have missing values in certain views, proposing TRUST-FS which simultaneously performs feature selection, imputation, and view weight learning, achieving superior performance over state-of-the-art methods.
Multi-view unsupervised feature selection (MUFS), which selects informative features from multi-view unlabeled data, has attracted increasing research interest in recent years. Although great efforts have been devoted to MUFS, several challenges remain: 1) existing methods for incomplete multi-view data are limited to handling missing views and are unable to address the more general scenario of missing variables, where some features have missing values in certain views; 2) most methods address incomplete data by first imputing missing values and then performing feature selection, treating these two processes independently and overlooking their interactions; 3) missing data can result in an inaccurate similarity graph, which reduces the performance of feature selection. To solve this dilemma, we propose a novel MUFS method for incomplete multi-view data with missing variables, termed Tensorized Reliable UnSupervised mulTi-view Feature Selection (TRUST-FS). TRUST-FS introduces a new adaptive-weighted CP decomposition that simultaneously performs feature selection, missing-variable imputation, and view weight learning within a unified tensor factorization framework. By utilizing Subjective Logic to acquire trustworthy cross-view similarity information, TRUST-FS facilitates learning a reliable similarity graph, which subsequently guides feature selection and imputation. Comprehensive experimental results demonstrate the effectiveness and superiority of our method over state-of-the-art methods.