Multi-View Spectral Clustering on Conflicting Views
Abstract
In a growing number of application domains, multiple feature representations or views are available to describe objects. Multi-view clustering tries to find similar groups of objects across these views. This task is complicated when the corresponding clusterings in each view show poor agreement ( conflicting views). In such cases, traditional multi-view clustering methods will not benefit from using multi-view data. Here, we propose to overcome this problem by combining the ideas of multi-view spectral clustering with alternative clustering through kernel-based dimensionality reduction. Our method automatically determines feature transformations in each view that lead to an optimal clustering w.r.t to a new proposed objective function for conflicting views. In our experiments, our approach outperforms state-of-the-art multi-view clustering methods by more accurately detecting the ground truth clustering supported by all views.
Cite
Text
He et al. "Multi-View Spectral Clustering on Conflicting Views." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_50Markdown
[He et al. "Multi-View Spectral Clustering on Conflicting Views." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/he2017ecmlpkdd-multiview/) doi:10.1007/978-3-319-71246-8_50BibTeX
@inproceedings{he2017ecmlpkdd-multiview,
title = {{Multi-View Spectral Clustering on Conflicting Views}},
author = {He, Xiao and Li, Limin and Roqueiro, Damian and Borgwardt, Karsten M.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {826-842},
doi = {10.1007/978-3-319-71246-8_50},
url = {https://mlanthology.org/ecmlpkdd/2017/he2017ecmlpkdd-multiview/}
}