Deep clustering based on a mixture of autoencodersDownload PDF

27 Sept 2018 (modified: 05 May 2023)ICLR 2019 Conference Withdrawn SubmissionReaders: Everyone
Abstract: In this paper we propose a Deep Autoencoder Mixture Clustering (DAMIC) algorithm. It is based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. The clustering algorithm jointly learns the nonlinear data representation and the set of autoencoders. The optimal clustering is found by minimizing the reconstruction loss of the mixture of autoencoder network. Unlike other deep clustering algorithms, no regularization term is needed to avoid data collapsing to a single point. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.
Keywords: deep clustering, mixture of experts, mixture of autoencoders
TL;DR: We propose a deep clustering method where instead of a centroid each cluster is represented by an autoencoder
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