Modified minimum squared error algorithm for robust classification and face recognition experiments

Yong Xu, Xiaozhao Fang, Qi Zhu, Yan Chen, Jia You, Hong Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


In this paper, we improve the minimum squared error (MSE) algorithm for classification by modifying its classification rule. Differing from the conventional MSE algorithm which first obtains the mapping that can best transform the training sample into its class label and then exploits the obtained mapping to predict the class label of the test sample, the modified minimum squared error classification (MMSEC) algorithm simultaneously predicts the class labels of the test sample and the training samples nearest to it and combines the predicted results to ultimately classify the test sample. Besides this paper, for the first time, proposes the idea to take advantage of the predicted class labels of the training samples for classification of the test sample, it devises a weighted fusion scheme to fuse the predicted class labels of the training sample and test sample. The paper also interprets the rationale of MMSEC. As MMSEC generalizes better than conventional MSE, it can lead to more robust classification decisions. The face recognition experiments show that MMSEC does obtain very promising performance.
Original languageEnglish
Pages (from-to)253-261
Number of pages9
Publication statusPublished - 5 Jul 2014


  • Face recognition
  • Minimum squared error (MSE)
  • Pattern recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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