Self-Supervised Deep Low-Rank Assignment Model for Prototype Selection

Prototype selection is a promising technique for removing redundancy and irrelevance from large-scale data. Here, we consider it as a task assignment problem, which refers to assigning each element of a source set to one representative, i. e. , prototype. However, due to the outliers and uncertain distribution on source, the selected prototypes are generally less representative and interesting. To alleviate this issue, we develop in this paper a Self-supervised Deep Low-rank Assignment model (SDLA). By dynamically integrating a low-rank assignment model with deep representation learning, our model effectively ensures the goodness-of-exemplar and goodness-of-discrimination of selected prototypes. Specifically, on the basis of a denoising autoencoder, dissimilarity metrics on source are continuously self-refined in embedding space with weak supervision from selected prototypes, thus preserving categorical similarity. Conversely, working on this metric space, similar samples tend to select the same prototypes by designing a low-rank assignment model. Experimental results on applications like text clustering and image classification (using prototypes) demonstrate our method is considerably superior to the state-of-the-art methods in prototype selection.

Authors

• Xingxing Zhang School of Computer Science, Tsinghua University, Beijing, China.
• Zhenfeng Zhu
• Yao Zhao Institute of Information Science, Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology
• Deqiang Kong

Keywords

• Machine Learning: Active Learning
• Machine Learning: Clustering
• Machine Learning: Semi-Supervised Learning