ICML 2016poster4003 citations
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, Ali Farhadi
Abstract
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.
BibTeX
@InProceedings{pmlr-v48-xieb16,
title = {Unsupervised Deep Embedding for Clustering Analysis},
author = {Xie, Junyuan and Girshick, Ross and Farhadi, Ali},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {478--487},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/xieb16.pdf},
url = {https://proceedings.mlr.press/v48/xieb16.html},
abstract = {Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.}
}