Research Breakthrough Possible @S-Logix
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Research Area: Machine Learning
Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering performance. To address this drawback, we propose s elf-supervised discriminative feature learning for d eep m ulti- v iew c lustering (SDMVC). Concretely, deep autoencoders are applied to learn embedded features for each view independently. To leverage the multi-view complementary information, we concatenate all views embedded features to form the global features, which can overcome the negative impact of some views unclear clustering structures. In a self-supervised manner, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning. During this process, global discriminative information can be mined to supervise all views to learn more discriminative features, which in turn are used to update the target distribution. Besides, this unified target distribution can make SDMVC learn consistent cluster assignments, which accomplishes the clustering consistency of multiple views while preserving their features diversity. Experiments on various types of multi-view datasets show that SDMVC outperforms 14 competitors including classic and state-of-the-art methods.
Keywords:
Representation learning
Task analysis
Matrix decomposition
Decoding
Computer science
Complexity theory
Unsupervised learning
Author(s) Name: Jie Xu; Yazhou Ren; Huayi Tang; Zhimeng Yang
Journal name: IEEE Transactions on Knowledge and Data Engineering
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TKDE.2022.3193569
Volume Information: Volume: 35
