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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Related Experiment Video

Updated: Sep 30, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Unsupervised Feature Selection via Graph Regularized Nonnegative CP Decomposition.

Bilian Chen, Jiewen Guan, Zhening Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 17, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel unsupervised feature selection method, CPUFS, designed for multi-dimensional tensor data. CPUFS effectively preserves tensor structure, outperforming existing methods on benchmark datasets.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Tensor Analysis

    Background:

    • Unsupervised feature selection is crucial for high-dimensional data.
    • Existing methods struggle with multi-dimensional tensor data due to information loss during vectorization.
    • Preserving multi-dimensional structural information is key for effective feature selection.

    Purpose of the Study:

    • To propose a novel unsupervised feature selection model for tensor data.
    • To overcome the limitations of traditional non-tensor-based methods.
    • To develop a method that fully preserves the multi-dimensional structure of tensor data.

    Main Methods:

    • Proposed CPUFS (Nonnegative tensor CP decomposition based unsupervised feature selection) model.
    • Devised tensor-oriented linear classifier and feature selection matrix.
    • Employed graph regularized nonnegative CP decomposition and tensor-oriented pseudo label regression.
    • Developed an efficient iterative optimization algorithm with guaranteed convergence.

    Main Results:

    • CPUFS effectively preserves multi-dimensional tensor data structure.
    • The proposed method demonstrates superior performance compared to state-of-the-art techniques.
    • A variation, CPUFSnn, incorporating nonnegativity into the linear classifier, was also studied.

    Conclusions:

    • CPUFS offers an effective solution for unsupervised feature selection on tensor data.
    • The tensor-based approach significantly improves feature selection quality.
    • The proposed method shows strong potential for real-world applications involving multi-dimensional data.