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A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure.

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    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    This summary is machine-generated.

    Feature subset selection uses mutual information as a measure. A new parallel search strategy based on semidefinite programming (SDP) offers a more efficient approach than traditional methods for high-dimensional data mining.

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    Area of Science:

    • Data Mining
    • Machine Learning
    • Computational Statistics

    Background:

    • Feature subset selection is crucial for data mining with high-dimensional data.
    • Existing methods face challenges in computation speed and robustness for measure functions and search strategies.
    • Mutual information is a key measure, but its efficient computation and optimization remain complex.

    Purpose of the Study:

    • To propose novel series expansions for mutual information as a robust measure function.
    • To introduce a parallel search strategy using semidefinite programming (SDP) for efficient subset optimization.
    • To analyze the approximation ratio of the proposed SDP-based algorithm and compare it with existing methods.

    Main Methods:

    • Development of two series expansions for mutual information.
    • Implementation of a parallel search strategy leveraging semidefinite programming (SDP).
    • Derivation and comparison of approximation ratios with backward elimination, relating to the maximum-cut problem.

    Main Results:

    • Proposed mutual information expansions encompass many existing heuristic criteria as approximations.
    • The SDP-based parallel search strategy operates in polynomial time, outperforming NP-hard search complexities.
    • Experimental results indicate that classification accuracy alone can be misleading without considering search strategy effectiveness.

    Conclusions:

    • Mutual information series expansions provide a theoretical foundation for feature selection measures.
    • Semidefinite programming offers a powerful, efficient parallel search framework for feature subset selection.
    • Evaluating feature selection methods requires considering both the measure and the search strategy's impact on performance.