Learning distance metrics with dimension constraints

FANG Juan; LIU Hong-ying; LI Qing-li

doi:10.3969/j.issn.1000-5641.2017.02.009

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Mar. 2017

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FANG Juan, LIU Hong-ying, LI Qing-li. Learning distance metrics with dimension constraints[J]. Journal of East China Normal University (Natural Sciences), 2017, (2): 69-74, 88. doi: 10.3969/j.issn.1000-5641.2017.02.009

Citation:

FANG Juan, LIU Hong-ying, LI Qing-li. Learning distance metrics with dimension constraints[J]. Journal of East China Normal University (Natural Sciences), 2017, (2): 69-74, 88. doi: 10.3969/j.issn.1000-5641.2017.02.009

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Learning distance metrics with dimension constraints

doi: 10.3969/j.issn.1000-5641.2017.02.009

School of Information Technology, Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China

Received Date: 2015-07-08
Publish Date: 2017-03-25

Abstract

Abstract

In order to improve the classification accuracy, the new representation of samples can be gotten by distance metric learning. According to mahalanobis distance does not take the difference of the relativity between different classes of sample dimensions into consideration. A new supervised distance metric learning algorithm called independent discrimi-native component analysis (I-DCA) is proposed and applied to classify the motor and sensory nerve based on k nearest neighbor (kNN) algorithm. By contrast, the article also involves the analysis of two existing distance metric learning algorithms in detail, the relevant component analysis (RCA) and the discrimi-native component analysis (DCA). Compared with the mahalanobis distance, the results indicate that the classification precision of the improved algorithm increases by nearly 45%, and it is also greater than 15% compared to the RCA and DCA method. The improved classification precision shows the effectiveness of the new algorithm applied in nerve classification.
- distance metric learning,
- supervised learning,
- kNN classification,
- microscopic hyperspectral imaging,
- neural classification

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References(12)

References

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