Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Robust Representations for Computer Vision   Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
  Our main goal is deeper understanding and new development of robust
approaches for representation learning. We provide a new interpretation for
existing robust approaches and present two specific contributions: a new robust
PCA approach, which can separate foreground features from dynamic background,
and a novel robust spectral clustering method, that can cluster facial images
with high accuracy. Both contributions show superior performance to standard
methods on real-world test sets.

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The categories of this paper are: eess
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con