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13th International Conference on Computer and Knowledge Engineering
Information Theoretic Learning-based Deep Embedded Clustering (ITL-DEC)
Authors :
Hoda Shad
1
Mona Zamiri
2
Tahereh Bahreini
3
Reza Monsefi
4
Ghoshe Abed Hodtani
5
1- Computer Engineering Department Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Square, Mashad, Iran
2- Computer Engineering Department Faculty of Wayne State University
3- Department of Electrical Engineering Ferdowsi University of Mashhad Mashhad, Iran
4- Computer Engineering Department Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Square, Mashad, Iran
5- Department of Electrical Engineering Ferdowsi University of Mashhad Mashhad, Iran
Keywords :
Clustering،Deep Neural Networks،Autoencoder،Representation Learning،Unsupervised Learning،Cauchy-Schwarz Divergence،Jenson-shanon Divergence،Deep Clustering
Abstract :
Clustering as the best grouping algorithm for the data sets is a fundamental problem in many data-driven scientific and real-world applications. There are several methods based on some similarity measures for clustering, all suffering from high computational complexity on large-scale datasets. Clustering performance highly depends on the quality of data representation; hence, in the literature, various linear and nonlinear representation methods and deep learning-based clustering algorithms have been exploited. This paper presents a novel fully unsupervised deep clustering method with end-to-end training capable of simultaneously learning feature representations and cluster assignments using deep neural networks. We use autoencoder as our powerful feature extraction deep neural network and two information-theoretic divergence measures, Cauchy-Schwarz divergence and Jensen-Shannon divergence, as cost functions to train the network parameter and appropriate clustering feature space. Experiments performed on the benchmark data sets validate the effectiveness of the proposed method.
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