Semisupervised Feature Learning by Deep Entropy-Sparsity Subspace Clustering

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 2; pp. 774 - 788
• Main Authors: Wu, Sheng, Zheng, Wei-Shi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Clustering; Constraints; Convolution; Data models; Entropy; Feature extraction; Image reconstruction; Learning; Machine learning; Neural networks; Self-similarity; Semisupervised convolutional neural network; Sparse matrices; sparse subspace clustering; Sparsity; Subspaces; Task analysis
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2020.3029033

While feature learning by deep neural networks is currently widely used, it is still very challenging to perform this task, given the very limited quantity of labeled data. To solve this problem, we propose to unite subspace clustering with deep semisupervised feature learning to form a unified learning framework to pursue feature learning by subspace clustering. More specifically, we develop a deep entropy-sparsity subspace clustering (deep ESSC) model, which forces a deep neural network to learn features using subspace clustering constrained by our designed entropy-sparsity scheme. The model can inherently harmonize deep semisupervised feature learning and subspace clustering simultaneously by the proposed self-similarity preserving strategy. To optimize the deep ESSC model, we introduce two unconstrained variables to eliminate the two constraints via softmax functions. We provide a general algebraic-treatment scheme for solving the proposed deep ESSC model. Extensive experiments with comprehensive analysis substantiate that our deep ESSC model is more effective than the related methods.