Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spectral Perturbation Meets Incomplete Multi-view Data   Beyond existing multi-view clustering, this paper studies a more realistic
clustering scenario, referred to as incomplete multi-view clustering, where a
number of data instances are missing in certain views. To tackle this problem,
we explore spectral perturbation theory. In this work, we show a strong link
between perturbation risk bounds and incomplete multi-view clustering. That is,
as the similarity matrix fed into spectral clustering is a quantity bounded in
magnitude O(1), we transfer the missing problem from data to similarity and
tailor a matrix completion method for incomplete similarity matrix. Moreover,
we show that the minimization of perturbation risk bounds among different views
maximizes the final fusion result across all views. This provides a solid
fusion criteria for multi-view data. We motivate and propose a
Perturbation-oriented Incomplete multi-view Clustering (PIC) method.
Experimental results demonstrate the effectiveness of the proposed method.

11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex