K-Autoencoders Deep Clustering
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING(2020)
摘要
In this study we propose a deep clustering algorithm that extends the k-means algorithm. Each cluster is represented by an autoencoder instead of a single centroid vector. Each data point is associated with the autoencoder which yields the minimal reconstruction error. The optimal clustering is found by learning a set of autoencoders that minimize the global reconstruction mean-square error loss. The network architecture is a simplified version of a previous method that is based on mixture-of-experts. The proposed method is evaluated on standard image corpora and performs on par with state-of-the-art methods which are based on much more complicated network architectures.
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关键词
clustering, autoencoders, deep networks
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