SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Abstract

As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.

Correction: Yi et al. SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information. Information 2024, 15, 57

“…In the original publication [1], there were two errors in Table 3 as published. Specifically, there was a mistake in the algorithm complexity of SFS-AGGL and a mistake of the order of reference for FDEFS method.…”

“…In the original publication [1], there were errors in Table 4 as published. Specifically, mistakes were made in the first-order derivatives of F, the second-order derivatives of F, and S. The corrected version of Table 4 is provided below.…”

“…The corrected equations are provided below. In the original publication [1], there were some mistakes in sub-images of Figure 7 as published. Specifically, errors were made in the feature dimensions of the sub-images in Figure 7.…”

“…There were errors in some equations in the original publication [1]. Specifically, mistakes were made in the definition of symbol, the matrix transposition operation, or the absence of the matrix trace operation.…”

“…In the original publication [1], there were errors in Algorithm 1 as published. Specifically, mistakes were made in the calculation and update of the matrices.…”