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» Parallel matrix algorithms and applications
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KDD
2004
ACM
158views Data Mining» more  KDD 2004»
14 years 10 months ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...
ICS
2007
Tsinghua U.
14 years 4 months ago
Adaptive Strassen's matrix multiplication
Strassen’s matrix multiplication (MM) has benefits with respect to any (highly tuned) implementations of MM because Strassen’s reduces the total number of operations. Strasse...
Paolo D'Alberto, Alexandru Nicolau
BMCBI
2010
155views more  BMCBI 2010»
13 years 10 months ago
A flexible R package for nonnegative matrix factorization
Background: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face re...
Renaud Gaujoux, Cathal Seoighe
HPCC
2005
Springer
14 years 3 months ago
Fast Sparse Matrix-Vector Multiplication by Exploiting Variable Block Structure
Abstract. We improve the performance of sparse matrix-vector multiplication (SpMV) on modern cache-based superscalar machines when the matrix structure consists of multiple, irregu...
Richard W. Vuduc, Hyun-Jin Moon
IPPS
1999
IEEE
14 years 2 months ago
Reducing I/O Complexity by Simulating Coarse Grained Parallel Algorithms
Block-wise access to data is a central theme in the design of efficient external memory (EM) algorithms. A second important issue, when more than one disk is present, is fully par...
Frank K. H. A. Dehne, David A. Hutchinson, Anil Ma...