A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models

Ajit V. Rao, David J. Miller, Kenneth Rose, Alien Gersho

    Research output: Contribution to journalArticle

    45 Scopus citations

    Abstract

    A new learning algorithm is proposed for piecewise regression modeling. It employs the technique of deterministic annealing to design space partition regression functions. While the performance of traditional space partition regression functions such as CART and MARS is limited by a simple tree-structured partition and by a hierarchical approach for design the deterministic annealing algorithm enables the joint optimization of a more powerful piecewise structure based on a Voronoi partition. The new method is demonstrated to achieve consistent performance improvements over regular CART as well as over its extension to allow arbitrary hyperplane boundaries. Comparison tests on several benchmark data sets from the regression literature are provided.

    Original languageEnglish (US)
    Pages (from-to)159-173
    Number of pages15
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume21
    Issue number2
    DOIs
    Publication statusPublished - Dec 1 1999

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    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition
    • Computational Theory and Mathematics
    • Artificial Intelligence
    • Applied Mathematics

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