Large-scale Multi-view Subspace Clustering in Linear Time

By Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu. AAAI Conference on Artificial Intelligence, 2019

602 citations recorded by the source corpus.

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Urban Studies

View Paper: https://www.semanticscholar.org/paper/80d687267eaa20adabeff59ca36bb1992176eea7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. En Zhu
2. Siwei Wang
3. Wenxuan Tu
4. Xinwang Liu
5. Pei Zhang
6. Yi Zhang
7. Feng Gao
8. Sihang Zhou

In Statistical and Nonlinear Physics

1. Xinwang Liu

Citations in the corpus

• Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, neutral): “like clustering [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], classification [14], [15], [16], [17], and human action recognition [18].”
• Scalable Multi-view Subspace Clustering with Unified Anchors (2021, methodology): “Large-scale Multiview Subspace Clustering in Linear Time (LMVSC) [11] is designed to handle large-scale data and has linear complexity.”
• Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview (2021, neutral): “To be precise, LMVSC does not consider the structure of graph and distinguish views, so it is a one-pass approach.”
• Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, methodology): “Therefore, we adopt the anchor strategy [8], [22] to select a small set of data points called anchor points or landmarks to reconstruct the underlying subspace and capture the manifold structure.”