Multi-View Clustering and Feature Learning via Structured Sparsity

Hua Wang, Feiping Nie, Heng Huang
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):352-360, 2013.

Abstract

Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms. The proposed multi-view learning framework allows us not only to perform clustering tasks, but also to deal with classification tasks by an extension when the labeling knowledge is available. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergence. We applied our new data fusion method to five broadly used multi-view data sets for both clustering and classification. In all experimental results, our method clearly outperforms other related state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-wang13c, title = {Multi-View Clustering and Feature Learning via Structured Sparsity}, author = {Wang, Hua and Nie, Feiping and Huang, Heng}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {352--360}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/wang13c.pdf}, url = {https://proceedings.mlr.press/v28/wang13c.html}, abstract = {Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms. The proposed multi-view learning framework allows us not only to perform clustering tasks, but also to deal with classification tasks by an extension when the labeling knowledge is available. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergence. We applied our new data fusion method to five broadly used multi-view data sets for both clustering and classification. In all experimental results, our method clearly outperforms other related state-of-the-art methods.} }
Endnote
%0 Conference Paper %T Multi-View Clustering and Feature Learning via Structured Sparsity %A Hua Wang %A Feiping Nie %A Heng Huang %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-wang13c %I PMLR %P 352--360 %U https://proceedings.mlr.press/v28/wang13c.html %V 28 %N 3 %X Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms. The proposed multi-view learning framework allows us not only to perform clustering tasks, but also to deal with classification tasks by an extension when the labeling knowledge is available. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergence. We applied our new data fusion method to five broadly used multi-view data sets for both clustering and classification. In all experimental results, our method clearly outperforms other related state-of-the-art methods.
RIS
TY - CPAPER TI - Multi-View Clustering and Feature Learning via Structured Sparsity AU - Hua Wang AU - Feiping Nie AU - Heng Huang BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/26 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-wang13c PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 3 SP - 352 EP - 360 L1 - http://proceedings.mlr.press/v28/wang13c.pdf UR - https://proceedings.mlr.press/v28/wang13c.html AB - Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms. The proposed multi-view learning framework allows us not only to perform clustering tasks, but also to deal with classification tasks by an extension when the labeling knowledge is available. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergence. We applied our new data fusion method to five broadly used multi-view data sets for both clustering and classification. In all experimental results, our method clearly outperforms other related state-of-the-art methods. ER -
APA
Wang, H., Nie, F. & Huang, H.. (2013). Multi-View Clustering and Feature Learning via Structured Sparsity. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(3):352-360 Available from https://proceedings.mlr.press/v28/wang13c.html.

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