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ICPR
2002
IEEE
14 years 10 months ago
A Fast Leading Eigenvector Approximation for Segmentation and Grouping
We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is base...
Antonio Robles-Kelly, Sudeep Sarkar, Edwin R. Hanc...
FCSC
2010
108views more  FCSC 2010»
13 years 7 months ago
On the computation of quotients and factors of regular languages
Quotients and factors are important notions in the design of various computational procedures for regular languages and for the analysis of their logical properties. We propose a n...
Mircea Marin, Temur Kutsia
SIGMOD
2008
ACM
157views Database» more  SIGMOD 2008»
14 years 9 months ago
CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition
The problem of simultaneously clustering columns and rows (coclustering) arises in important applications, such as text data mining, microarray analysis, and recommendation system...
Feng Pan, Xiang Zhang, Wei Wang 0010

Publication
197views
12 years 5 months ago
Convex non-negative matrix factorization for massive datasets
Non-negative matrix factorization (NMF) has become a standard tool in data mining, information retrieval, and signal processing. It is used to factorize a non-negative data matrix ...
C. Thurau, K. Kersting, M. Wahabzada, and C. Bauck...
SIAMJO
2011
12 years 11 months ago
Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-ran...
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Pa...