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» Algorithms for Non-negative Matrix Factorization
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ICASSP
2011
IEEE
12 years 11 months ago
Low-rank matrix completion by variational sparse Bayesian learning
There has been a significant interest in the recovery of low-rank matrices from an incomplete of measurements, due to both theoretical and practical developments demonstrating th...
S. Derin Babacan, Martin Luessi, Rafael Molina, Ag...
BIBE
2007
IEEE
159views Bioinformatics» more  BIBE 2007»
13 years 11 months ago
Non-negative Tensor Factorization Based on Alternating Large-scale Non-negativity-constrained Least Squares
Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) have attracted much attention and have been successfully applied to numerous data analysis probl...
Hyunsoo Kim, Haesun Park, Lars Eldén
CIKM
2011
Springer
12 years 7 months ago
Factorization-based lossless compression of inverted indices
Many large-scale Web applications that require ranked top-k retrieval are implemented using inverted indices. An inverted index represents a sparse term-document matrix, where non...
George Beskales, Marcus Fontoura, Maxim Gurevich, ...
PC
2007
133views Management» more  PC 2007»
13 years 7 months ago
Data distribution for dense factorization on computers with memory heterogeneity
In this paper, we study the problem of optimal matrix partitioning for parallel dense factorization on heterogeneous processors. First, we outline existing algorithms solving the ...
Alexey L. Lastovetsky, Ravi Reddy
DAC
1999
ACM
14 years 2 days ago
ENOR: Model Order Reduction of RLC Circuits Using Nodal Equations for Efficient Factorization
ENOR is an innovative way to produce provablypassive, reciprocal, and compact representations of RLC circuits. Beginning with the nodal equations, ENOR formulates recurrence relat...
Bernard N. Sheehan