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• W3035575892 abstract "In this paper, we introduce the Dissimilarity Mixture Autoencoder (DMAE), a novel neural network model that uses a dissimilarity function to generalize a family of density estimation and clustering methods. It is formulated in such a way that it internally estimates the parameters of a probability distribution through gradient-based optimization. Also, the proposed model can leverage from deep representation learning due to its straightforward incorporation into deep learning architectures, because, it consists of an encoder-decoder network that computes a probabilistic representation. Experimental evaluation was performed on image and text clustering benchmark datasets showing that the method is competitive in terms of unsupervised classification accuracy and normalized mutual information. The source code to replicate the experiments is publicly available at this https URL" @default.
• W3035575892 created "2020-06-19" @default.
• W3035575892 creator A5003683641 @default.
• W3035575892 creator A5080973347 @default.
• W3035575892 date "2020-06-15" @default.
• W3035575892 modified "2023-09-27" @default.
• W3035575892 title "Dissimilarity Mixture Autoencoder for Deep Clustering." @default.
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• W3035575892 hasPublicationYear "2020" @default.
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