ICML 2019oral374 citations
COMIC: Multi-view Clustering Without Parameter Selection
Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou
Abstract
In this paper, we study two challenges in clustering analysis, namely, how to cluster multi-view data and how to perform clustering without parameter selection on cluster size. To this end, we propose a novel objective function to project raw data into one space in which the projection embraces the geometric consistency (GC) and the cluster assignment consistency (CAC). To be specific, the GC aims to learn a connection graph from a projection space wherein the data points are connected if and only if they belong to the same cluster. The CAC aims to minimize the discrepancy of pairwise connection graphs induced from different views based on the view-consensus assumption,
BibTeX
@InProceedings{pmlr-v97-peng19a,
title = {{COMIC}: Multi-view Clustering Without Parameter Selection},
author = {Peng, Xi and Huang, Zhenyu and Lv, Jiancheng and Zhu, Hongyuan and Zhou, Joey Tianyi},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {5092--5101},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/peng19a/peng19a.pdf},
url = {https://proceedings.mlr.press/v97/peng19a.html},
abstract = {In this paper, we study two challenges in clustering analysis, namely, how to cluster multi-view data and how to perform clustering without parameter selection on cluster size. To this end, we propose a novel objective function to project raw data into one space in which the projection embraces the geometric consistency (GC) and the cluster assignment consistency (CAC). To be specific, the GC aims to learn a connection graph from a projection space wherein the data points are connected if and only if they belong to the same cluster. The CAC aims to minimize the discrepancy of pairwise connection graphs induced from different views based on the view-consensus assumption,