Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Robust Subspace Clustering   We propose a low-rank transformation-learning framework to robustify subspace
clustering. Many high-dimensional data, such as face images and motion
sequences, lie in a union of low-dimensional subspaces. The subspace clustering
problem has been extensively studied in the literature to partition such
high-dimensional data into clusters corresponding to their underlying
low-dimensional subspaces. However, low-dimensional intrinsic structures are
often violated for real-world observations, as they can be corrupted by errors
or deviate from ideal models. We propose to address this by learning a linear
transformation on subspaces using matrix rank, via its convex surrogate nuclear
norm, as the optimization criteria. The learned linear transformation restores
a low-rank structure for data from the same subspace, and, at the same time,
forces a high-rank structure for data from different subspaces. In this way, we
reduce variations within the subspaces, and increase separations between the
subspaces for more accurate subspace clustering. This proposed learned robust
subspace clustering framework significantly enhances the performance of
existing subspace clustering methods. To exploit the low-rank structures of the
transformed subspaces, we further introduce a subspace clustering technique,
called Robust Sparse Subspace Clustering, which efficiently combines robust PCA
with sparse modeling. We also discuss the online learning of the
transformation, and learning of the transformation while simultaneously
reducing the data dimensionality. Extensive experiments using public datasets
are presented, showing that the proposed approach significantly outperforms
state-of-the-art subspace clustering methods.

21
The categories of this paper are: adap-org
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol