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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

Linearity-Aware Subspace Clustering

February 1, 2023

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Authors

Yesong Xu

Nanjing University of Science and Technology


Shuo Chen

Nanjing University of Science and Technology


Jun Li

Nanjing University of Science and Technology


Jianjun Qian

Nanjing University of Science and Technology


DOI:

10.1609/aaai.v36i8.20857


Abstract:

Obtaining a good similarity matrix is extremely important in subspace clustering. Current state-of-the-art methods learn the similarity matrix through self-expressive strategy. However, these methods directly adopt original samples as a set of basis to represent itself linearly. It is difficult to accurately describe the linear relation between samples in the real-world applications, and thus is hard to find an ideal similarity matrix. To better represent the linear relation of samples, we present a subspace clustering model, Linearity-Aware Subspace Clustering (LASC), which can consciously learn the similarity matrix by employing a linearity-aware metric. This is a new subspace clustering method that combines metric learning and subspace clustering into a joint learning framework. In our model, we first utilize the self-expressive strategy to obtain an initial subspace structure and discover a low-dimensional representation of the original data. Subsequently, we use the proposed metric to learn an intrinsic similarity matrix with linearity-aware on the obtained subspace. Based on such a learned similarity matrix, the inter-cluster distance becomes larger than the intra-cluster distances, and thus successfully obtaining a good subspace cluster result. In addition, to enrich the similarity matrix with more consistent knowledge, we adopt a collaborative learning strategy for self-expressive subspace learning and linearity-aware subspace learning. Moreover, we provide detailed mathematical analysis to show that the metric can properly characterize the linear correlation between samples.

Topics: AAAI

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HOW TO CITE:

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian Linearity-Aware Subspace Clustering Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian Linearity-Aware Subspace Clustering AAAI 2022, 8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian (2022). Linearity-Aware Subspace Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian. Linearity-Aware Subspace Clustering. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian. 2022. Linearity-Aware Subspace Clustering. "Proceedings of the AAAI Conference on Artificial Intelligence". 8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian. (2022) "Linearity-Aware Subspace Clustering", Proceedings of the AAAI Conference on Artificial Intelligence, p.8770-8778

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian, "Linearity-Aware Subspace Clustering", AAAI, p.8770-8778, 2022.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian. "Linearity-Aware Subspace Clustering". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian. "Linearity-Aware Subspace Clustering". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8770-8778.

Yesong Xu||Shuo Chen||Jun Li||Jianjun Qian. Linearity-Aware Subspace Clustering. AAAI[Internet]. 2022[cited 2023]; 8770-8778.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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