Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:1001-1016, 2019.
Abstract
As a new occurring unsupervised method, multi-view clustering offers a good way to investigate the hidden structure from multi-view data and attracts extensive attention in the community of machine learning and data mining. One popular approach is to identify a common latent subspace for capturing the multi-view information. However, these methods are still limited due to the unsupervised learning process and suffer from considerable noisy information from different views. To address this issue, we present a novel multi-view subspace clustering method, named self-supervised deep multi-view subspace clustering (\textbf{S2DMVSC}). It seamlessly integrates spectral clustering and affinity learning into a deep learning framework. \textbf{S2DMVSC} has two main merits. One is that the clustering results can be sufficiently exploited to supervise the latent representation learning for each view (via a classification loss) and the common latent subspace learning (via a spectral clustering loss) for multiple views. The other is that the affinity matrix among data objects is automatically computed according to the high-level and cluster-driven representation. Experiments on two scenarios, including original features and multiple hand-crafted features, demonstrate the superiority of the proposed approach over the state-of-the-art baselines.
@InProceedings{pmlr-v101-sun19a,
title = {Self-Supervised Deep Multi-View Subspace Clustering},
author = {Sun, Xiukun and Cheng, Miaomiao and Min, Chen and Jing, Liping},
booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning},
pages = {1001--1016},
year = {2019},
editor = {Lee, Wee Sun and Suzuki, Taiji},
volume = {101},
series = {Proceedings of Machine Learning Research},
address = {Nagoya, Japan},
month = {17--19 Nov},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v101/sun19a/sun19a.pdf},
url = {http://proceedings.mlr.press/v101/sun19a.html},
abstract = {As a new occurring unsupervised method, multi-view clustering offers a good way to investigate the hidden structure from multi-view data and attracts extensive attention in the community of machine learning and data mining. One popular approach is to identify a common latent subspace for capturing the multi-view information. However, these methods are still limited due to the unsupervised learning process and suffer from considerable noisy information from different views. To address this issue, we present a novel multi-view subspace clustering method, named self-supervised deep multi-view subspace clustering (\textbf{S2DMVSC}). It seamlessly integrates spectral clustering and affinity learning into a deep learning framework. \textbf{S2DMVSC} has two main merits. One is that the clustering results can be sufficiently exploited to supervise the latent representation learning for each view (via a classification loss) and the common latent subspace learning (via a spectral clustering loss) for multiple views. The other is that the affinity matrix among data objects is automatically computed according to the high-level and cluster-driven representation. Experiments on two scenarios, including original features and multiple hand-crafted features, demonstrate the superiority of the proposed approach over the state-of-the-art baselines.}
}
%0 Conference Paper
%T Self-Supervised Deep Multi-View Subspace Clustering
%A Xiukun Sun
%A Miaomiao Cheng
%A Chen Min
%A Liping Jing
%B Proceedings of The Eleventh Asian Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2019
%E Wee Sun Lee
%E Taiji Suzuki
%F pmlr-v101-sun19a
%I PMLR
%J Proceedings of Machine Learning Research
%P 1001--1016
%U http://proceedings.mlr.press
%V 101
%W PMLR
%X As a new occurring unsupervised method, multi-view clustering offers a good way to investigate the hidden structure from multi-view data and attracts extensive attention in the community of machine learning and data mining. One popular approach is to identify a common latent subspace for capturing the multi-view information. However, these methods are still limited due to the unsupervised learning process and suffer from considerable noisy information from different views. To address this issue, we present a novel multi-view subspace clustering method, named self-supervised deep multi-view subspace clustering (\textbf{S2DMVSC}). It seamlessly integrates spectral clustering and affinity learning into a deep learning framework. \textbf{S2DMVSC} has two main merits. One is that the clustering results can be sufficiently exploited to supervise the latent representation learning for each view (via a classification loss) and the common latent subspace learning (via a spectral clustering loss) for multiple views. The other is that the affinity matrix among data objects is automatically computed according to the high-level and cluster-driven representation. Experiments on two scenarios, including original features and multiple hand-crafted features, demonstrate the superiority of the proposed approach over the state-of-the-art baselines.
Sun, X., Cheng, M., Min, C. & Jing, L.. (2019). Self-Supervised Deep Multi-View Subspace Clustering. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:1001-1016
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