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Enhanced piecewise regression based on deterministic annealing

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Abstract

Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced piecewise regression algorithm by using “migration of prototypes” is proposed to eliminate “empty cell” in the annealing process. Numerical experiments on several benchmark datasets show that the new algorithm can remove redundancy and improve generalization of the piecewise regression model.

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Reference

  1. Rose K. Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proc IEEE, 1998, 86(11): 2210–2239

    Article  Google Scholar 

  2. Rose K, Gurewitz E, Fox G. A deterministic annealing approach to clustering. Patt Recog Lett, 1990, 11: 589–594

    Article  MATH  Google Scholar 

  3. Persson P, Nordebo S, Claesson I. Design of discrete coefficient FIR filters by a fast entropy-directed deterministic annealing algorithm. IEEE Trans Sig Proc, 2005, 53(3): 1006–1014

    Article  MathSciNet  Google Scholar 

  4. Koulgi P, Regunathan S L, Rose K. Multiple description quantization by deterministic annealing. IEEE Trans Inf Theo, 2003, 49(8): 2067–2075

    Article  Google Scholar 

  5. Rao A V. A deterministic annealing approach for parsimonious design of piecewise regression models. IEEE Trans Patt Anal Mach Intell, 1999, 21(2): 159–172

    Article  Google Scholar 

  6. Hastie T, Tibshirani T, Friedman J, The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag, 2001

    MATH  Google Scholar 

  7. Briedman L. Classification and Regression Tree. Belmont, CA: Wadsworth Inc., 1984

    Google Scholar 

  8. Rao A V. Mixture of experts regression modeling by deterministic annealing. IEEE Trans Sig Proc, 1997, 45(11): 2811–2820

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute for Information and System Sciences, Faculty of Sciences, Xi’an Jiaotong University, Xi’an, 710049, China

    JiangShe Zhang, YuQian Yang & XiaoWen Chen

  2. State Key Laboratory of Resources & Environmental Information System, Institute of Geography, Chinese Academy of Sciences, Beijing, 100101, China

    ChengHu Zhou

Authors
  1. JiangShe Zhang
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  2. YuQian Yang
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  3. XiaoWen Chen
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  4. ChengHu Zhou
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Corresponding author

Correspondence to JiangShe Zhang.

Additional information

Supported by the National Natural Science Foundation of China (Grant Nos. 60675013 and 4022500) and the National Basic Research Program of China (973 Program) (Grant No. 2007CB311002)

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Zhang, J., Yang, Y., Chen, X. et al. Enhanced piecewise regression based on deterministic annealing. Sci. China Ser. F-Inf. Sci. 51, 1025–1038 (2008). https://doi.org/10.1007/s11432-008-0079-0

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  • DOI: https://doi.org/10.1007/s11432-008-0079-0

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