Anti-Noise Muiti-View Feature Selection With Sample Constraints

The challenge of the dimensional disaster in multi-view data is an ongoing and formidable issue. Current multi-view feature selection algorithms aim to reduce dimensions by learning a feature subset that effectively captures the overall information of the data, integrating the characteristics from multiple views. However, they often overlook the detrimental impact of noise in the data, which compromises the performance of multi-view feature selection and yields inefficient feature subsets. To address this problem, this paper proposes an anti-noise multi-view feature selection algorithm. In particular, we begin by combining least squares loss and regularization techniques to learn the relationship between the data and labels. Subsequently, we introduce sample constraints, including view weight and sample weight, as well as feature weight factors, into the objective function. This incorporation reduces the significance of noisy samples, thereby enhancing the algorithm’s ability to resist noise interference. In comparative evaluations with state-of-the-art algorithms, the proposed algorithm exhibits an average improvement of 4.42% in classification accuracy when applied to publicly available datasets with added noise
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