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.
Original language | English |
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Title of host publication | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4037-4041 |
Number of pages | 5 |
ISBN (Electronic) | 9781509066315 |
DOIs | |
State | Published - May 2020 |
Event | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2020-May |
ISSN (Print) | 1520-6149 |
Conference
Conference | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 |
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Country/Territory | Spain |
City | Barcelona |
Period | 4/05/20 → 8/05/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- autoencoders
- clustering
- deep networks