Fuzzy weighted sparse reconstruction error-steered semi-supervised learning for face recognition

Li Liu

doi:10.1007/s00371-019-01746-y

2019-09-21

PRE

DOI：10.1007/s00371-019-01746-y

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摘要

En 中文

Since the number of labeled data is limited in the semi-supervised learning settings, we propose a fuzzy weighted sparse reconstruction error-steered semi-supervised learning method for face recognition. The fuzzy membership functions are introduced to the reconstruction error calculation for the unlabeled data. A weight function is utilized to capture the locality property of data when learning the sparse coefficients. The fuzzy weighted sparse reconstruction error-steered semi-supervised learning not only inherits the advantages of sparse representation classification techniques and neighborhood methods, but also steers the reconstruction errors of unlabeled data. Experimental studies on well-known face image datasets demonstrate that the proposed method outperforms the comparative approaches.

Keyword:

Membership function

Sparse representation

Fuzzy

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期刊

Visual Computer

IF:

2.9

论文数:

4.7K

被引数:

6.5K

机构

shandong normal university

学者数:

1.0W

论文数: 8.2K

被引数: 3

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