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Label Distribution Feature Selection with Feature Weights Fusion and Local Label Correlations by Wenbin Qian; Qianzhi Ye; Yihui Li; Shiming Dai is a Computer Science article available to read on EtoBox.

What is Label Distribution Feature Selection with Feature Weights Fusion and Local Label Correlations about?

Label distribution learning, where each sample is associated with a distribution of description degree, suffers from the curse of dimensionality like other traditional learning paradigms. Feature selection as a pre-processing technique is commonly used to reduce the dimension of data. Currently, label distribution feature selection focuses on exploiting label correlations under the assumption that all samples share the same label correlations. However, the corresponding label correlations for different groups of samples may tend to be different in real-world tasks. To tackle the issue, this paper presents a novel approach named label distribution feature selection with feature weights fusion and local label correlations. First, instances are separated into several clusters regarded as local samples. Then, a two-strategy approach is presented based on the local samples. For the first, the feature weights obtained from all local samples are fused into uniform feature significance, which can effectively improve the feature discrimination. For the second, to reflect the influence of label correlations locally, a local correlation matrix is encoded via the label space of local samples a

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Author

Wenbin Qian; Qianzhi Ye; Yihui Li; Shiming Dai

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)