Data Dimensionality Reduction Method of Semi-supervised Isometric Mapping Based on Regularization

WANG Xianbao; CHEN Shiwen; YAO Minghai

doi:10.11999/JEIT150694

Volume 38 Issue 1

Jan. 2016

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WANG Xianbao, CHEN Shiwen, YAO Minghai. Data Dimensionality Reduction Method of Semi-supervised Isometric Mapping Based on Regularization[J]. Journal of Electronics & Information Technology, 2016, 38(1): 241-245. doi: 10.11999/JEIT150694

Citation:

WANG Xianbao, CHEN Shiwen, YAO Minghai. Data Dimensionality Reduction Method of Semi-supervised Isometric Mapping Based on Regularization[J]. Journal of Electronics & Information Technology, 2016, 38(1): 241-245. doi: 10.11999/JEIT150694

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Data Dimensionality Reduction Method of Semi-supervised Isometric Mapping Based on Regularization

doi: 10.11999/JEIT150694

Funds:

Zhejiang Provincial Natural Science Foundation (LZ14F030001, LY14F030009)

Received Date: 2015-06-08
Rev Recd Date: 2015-09-21
Publish Date: 2016-01-19

Abstract

Abstract

This paper proposes Regularized Semi-Supervised ISOmetric MAPping (Reg-SS-ISOMAP) algorithm to solve the problem that ISOmetric MAPping (ISOMAP) algorithm is unsupervised and can not generate explicit mapping function. At first, this algorithm creates K-Connectivity Graph (K-CG) by labeled samples in training samples to get geodesic distance between approximate samples and takes it as feature vector substituting for original data. Then, it takes the geodesic distance as kernel and processes feature vector through semi-supervised regularization not MultiDimensional Scaling (MDS) algorithm. At last, it constructs objective function by regularization regression model which is low dimension and explicit mapping. The algorithm is simulated on different data sets, results show that it is stable in dimension reduction and high recognition rate.
- Data dimensionality reduction,
- Manifold learning,
- Semi-supervised learning,
- Regularization

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References(16)

References

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