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Multitask Learning for Sparse Failure Prediction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Consensus Graph Learning for Incomplete Multi-view Clustering

Authors : Wei Zhou, Hao Wang, Yan Yang

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all views. However, in real-world applications, it is common that certain views miss number of data instances, resulting in incomplete multi-view data. This paper concerns the task of clustering of incomplete multi-view data. We propose a novel Graph-based Incomplete Multi-view Clustering (GIMC) to perform this task. GIMC can effectively construct a complete graph for each view with the help of other view(s), and automatically weight each constructed graph to learn a consensus graph, which gives the final clusters. An alternating iterative optimization algorithm is proposed to optimize the objective function. Experimental results on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods markedly.

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previous chapter CytoFA: Automated Gating of Mass Cytometry Data via Robust Skew Factor Analzyers
next chapter Beyond Outliers and on to Micro-clusters: Vision-Guided Anomaly Detection
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Metadata
Title
Consensus Graph Learning for Incomplete Multi-view Clustering
Authors
Wei Zhou
Hao Wang
Yan Yang
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16147-7

Electronic ISBN: 978-3-030-16148-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16148-4_41

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