Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss

• URL: http://arxiv.org/abs/2412.08940v1
• Date: Thu, 12 Dec 2024 05:02:41 GMT
• Title: Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss
• Authors: Kart-Leong Lim,
• Abstract summary: In the autoencoder based deep clustering, the challenge is how to jointly optimize both clustering and dimension reduction together. We introduce an infinite cluster representation using Dirichlet process Gaussian mixture model for joint clustering and model selection in the latent space. We evaluate our proposed deep model selection method with traditional model selection on large class number datasets such as MIT67 and CIFAR100.
• Score: 0.65268245109828
• License:
• Abstract: Deep clustering is an emerging topic in deep learning where traditional clustering is performed in deep learning feature space. However, clustering and deep learning are often mutually exclusive. In the autoencoder based deep clustering, the challenge is how to jointly optimize both clustering and dimension reduction together, so that the weights in the hidden layers are not only guided by reconstruction loss, but also by a loss function associated with clustering. The current state-of-the-art has two fundamental flaws. First, they rely on the mathematical convenience of Kullback-Leibler divergence for the clustering loss function but the former is asymmetric. Secondly, they assume the prior knowledge on the number of clusters is always available for their dataset of interest. This paper tries to improve on these problems. In the first problem, we use a Jensen-Shannon divergence to overcome the asymmetric issue, specifically using a closed form variant. Next, we introduce an infinite cluster representation using Dirichlet process Gaussian mixture model for joint clustering and model selection in the latent space which we called deep model selection. The number of clusters in the latent space are not fixed but instead vary accordingly as they gradually approach the optimal number during training. Thus, prior knowledge is not required. We evaluate our proposed deep model selection method with traditional model selection on large class number datasets such as MIT67 and CIFAR100 and also compare with both traditional variational Bayes model and deep clustering method with convincing results.

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