Bach Tran, Hoai An Le Thi, Deep Clustering with Spherical Distance in Latent Space

Abstract: This paper studies the problem of deep joint-clustering using auto-encoder. For this task, most algorithms solve a multi-objective optimization problem, where it is then transformed into a sing-objective problem by linear scalarization techniques. However, it introduces the scaling problem in latent space in a class of algorithms. We propose an extension to solve this problem by using scale invariance distance functions. The advantage of this extension is demonstrated for a particular case of joint-clustering with MSSC (minimizing sum-of-squares clustering). Numerical experiments on several benchmark datasets illustrate the superiority of our extension over state-of-the-art algorithms with respect to clustering accuracy.


Keywords: Clustering, Deep learning, Auto-encoder, Spherical distance.


Citation: Tran B., Le Thi H.A. (2020) Deep Clustering with Spherical Distance in Latent Space. In: Le Thi H., Le H., Pham Dinh T., Nguyen N. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2019. Advances in Intelligent Systems and Computing, vol 1121, pp. 231-242. Springer, Cham.


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