Joint Sparse Representation and Embedding Propagation Learning: A Framework for Graph-Based Semisupervised Learning

Xiaobing Pei

doi:10.1109/TNNLS.2016.2609434

2017-12-01

PRE

Xiaobing Pei *

Chuanbo Chen

Yue Guan

DOI：10.1109/TNNLS.2016.2609434

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摘要

En 中文

In this paper, we propose a novel graph-based semisupervised learning framework, called joint sparse representation and embedding propagation learning (JSREPL). The idea of JSREPL is to join EPL with sparse representation to perform label propagation. Like most of graph-based semisupervised propagation learning algorithms, JSREPL also constructs weights graph matrix from given data. Different from classical approaches which build weights graph matrix and estimate the labels of unlabeled data in sequence, JSREPL simultaneously builds weights graph matrix and estimates the labels of unlabeled data. We also propose an efficient algorithm to solve the proposed problem. The proposed method is applied to the problem of semisupervised image clustering using the ORL, Yale, PIE, and YaleB data sets. Our experiments demonstrate the effectiveness of our proposed algorithm.

Keyword:

Label propagation (LP)

semisupervised learning

sparse representation

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期刊

IEEE Transactions on Neural Networks and Learning Systems

IF:

8.9

论文数:

7.5K

被引数:

7.2W

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