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Unsupervised Feature Selection via Controllable Adaptive Graph Learning and Discriminative Feature Learning.

Pei Huang, Mengying Xie, Xiaowei Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 4, 2023
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    Summary
    This summary is machine-generated.

    Two new unsupervised feature selection methods, controllable adaptive graph learning (CAG-U and CAG-I), address challenges in machine learning by adaptively learning graphs and selecting uncorrelated features. These methods improve upon existing techniques by controlling graph differences and reducing dimensionality.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Pattern Recognition

    Background:

    • Unsupervised feature selection is complex, requiring simultaneous preservation of intrinsic data structure and selection of uncorrelated features.
    • Existing methods often fail due to significant differences between initial and final graphs, requiring prior subspace dimension knowledge, and inefficiency with high-dimensional data.

    Purpose of the Study:

    • To propose novel unsupervised feature selection methods that overcome the limitations of current approaches.
    • To develop techniques that adaptively learn graphs while controlling differences and select relatively uncorrelated/independent features.

    Main Methods:

    • Controllable Adaptive Graph learning for Unsupervised feature selection (CAG-U).
    • Controllable Adaptive Graph learning for Independent feature selection (CAG-I).
    • Utilizing a discrete projection matrix for feature selection.

    Main Results:

    • The proposed CAG-U and CAG-I methods demonstrate superior performance compared to existing methods.
    • Experimental validation on 12 diverse datasets confirms the effectiveness of the new approaches.

    Conclusions:

    • CAG-U and CAG-I effectively address key challenges in unsupervised feature selection.
    • These methods offer improved performance and applicability across various fields by adaptively learning graphs and selecting uncorrelated/independent features.