Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cauchy Principal Component Analysis   Principal Component Analysis (PCA) has wide applications in machine learning,
text mining and computer vision. Classical PCA based on a Gaussian noise model
is fragile to noise of large magnitude. Laplace noise assumption based PCA
methods cannot deal with dense noise effectively. In this paper, we propose
Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective
PCA method which is robust to various types of noise. We utilize Cauchy
distribution to model noise and derive Cauchy PCA under the maximum likelihood
estimation (MLE) framework with low rank constraint. Our method can robustly
estimate the low rank matrix regardless of whether noise is large or small,
dense or sparse. We analyze the robustness of Cauchy PCA from a robust
statistics view and present an efficient singular value projection optimization
method. Experimental results on both simulated data and real applications
demonstrate the robustness of Cauchy PCA to various noise patterns.

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The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
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The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
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The categories of this paper are: econ
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
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The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg