Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Angular Embedding: A New Angular Robust Principal Component Analysis   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.

26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess