@inproceedings{f37b1ea82b914c449bacad66dc65892a,
title = "K-Autoencoders Deep Clustering",
abstract = "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-theart methods which are based on much more complicated network architectures.",
keywords = "autoencoders, clustering, deep networks",
author = "Yaniv Opochinsky and Chazan, {Shlomo E.} and Sharon Gannot and Jacob Goldberger",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
year = "2020",
month = may,
doi = "https://doi.org/10.1109/icassp40776.2020.9053109",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4037--4041",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
address = "الولايات المتّحدة",
}