Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Group $K$-Means   We study how to learn multiple dictionaries from a dataset, and approximate
any data point by the sum of the codewords each chosen from the corresponding
dictionary. Although theoretically low approximation errors can be achieved by
the global solution, an effective solution has not been well studied in
practice. To solve the problem, we propose a simple yet effective algorithm
\textit{Group $K$-Means}. Specifically, we take each dictionary, or any two
selected dictionaries, as a group of $K$-means cluster centers, and then deal
with the approximation issue by minimizing the approximation errors. Besides,
we propose a hierarchical initialization for such a non-convex problem.
Experimental results well validate the effectiveness of the approach.

34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
3
The categories of this paper are: physics