Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 32, Issue 6, Pages 1167-1178Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2901853
Keywords
Semisupervised learning; Feature extraction; Data models; Sparse matrices; Adaptation models; Correlation; Kernel; Graph-based semi-supervised learning; label propagation; auto-weighting feature; adaptive neighborhood
Categories
Funding
- National Natural Science Foundation of China [61772427, 61751202]
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Traditional graph-based Semi-Supervised Learning (SSL) methods usually contain two separate steps. First, constructing an affinity matrix. Second, inferring the unknown labels. While such a two-step method has been successful, it cannot take full advantage of the correlation between affinity matrix and label information. In order to address the above problem, we propose a novel graph-based SSL method. It can learn the affinity matrix and infer the unknown labels simultaneously. Moreover, feature selection with auto-weighting is introduced to extract the effective and robust features. Further, the proposed method learns the data similarity matrix by assigning the adaptive neighbors for each data point based on the local distance. We solve the unified problem via an alternative minimization algorithm. Extensive experimental results on synthetic data and benchmark data show that the proposed method consistently outperforms the state-of-the-art approaches.
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