Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification

Feiping Nie, Jing Li, Xuelong Li

科研成果: 期刊稿件会议文章同行评审

642 引用 (Scopus)

摘要

Graph-based approaches have been successful in unsupervised and semi-supervised learning. In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. The key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent manifold with the real data distributions. In this paper, we propose a novel framework via the reformulation of the standard spectral learning model, which can be used for multiview clustering and semisupervised tasks. Unlike other methods in the literature, the proposed methods can learn an optimal weight for each graph automatically without introducing an additive parameter as previous methods do. Furthermore, our objective under semisupervised learning is convex and the global optimal result will be obtained. Extensive empirical results on different real-world data sets demonstrate that the proposed methods achieve comparable performance with the state-of-the-art approaches and can be used more practically.

源语言英语
页(从-至)1881-1887
页数7
期刊IJCAI International Joint Conference on Artificial Intelligence
2016-January
出版状态已出版 - 2016
活动25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, 美国
期限: 9 7月 201615 7月 2016

指纹

探究 'Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification' 的科研主题。它们共同构成独一无二的指纹。

引用此