Angular Embedding: A New Angular Robust Principal Component Analysis

• URL: http://arxiv.org/abs/2011.11013v1
• Date: Sun, 22 Nov 2020 13:36:56 GMT
• Title: Angular Embedding: A New Angular Robust Principal Component Analysis
• Authors: Shenglan Liu, Yang Yu
• Abstract summary: It is a serious problem that principal component analysis is sensitive to outliers. The existing state-of-the-art RPCA approaches cannot easily remove or tolerate outliers by a non-iterative manner. This paper proposes Angular Embedding (AE) to formulate a straightforward RPCA approach based on angular density. Furthermore, a trimmed AE (TAE) is introduced to deal with data with large scale outliers.
• Score: 10.120548476934186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensitive to outliers, which has been improved by numerous Robust PCA (RPCA) versions. However, the existing state-of-the-art RPCA approaches cannot easily remove or tolerate outliers by a non-iterative manner. To tackle this issue, this paper proposes Angular Embedding (AE) to formulate a straightforward RPCA approach based on angular density, which is improved for large scale or high-dimensional data. Furthermore, a trimmed AE (TAE) is introduced to deal with data with large scale outliers. Extensive experiments on both synthetic and real-world datasets with vector-level or pixel-level outliers demonstrate that the proposed AE/TAE outperforms the state-of-the-art RPCA based methods.

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