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AAAI
2010
13 years 11 months ago
Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative fil...
Ian Porteous, Arthur Asuncion, Max Welling
NIPS
2008
13 years 11 months ago
Regularized Co-Clustering with Dual Supervision
By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clustering algorithms often demonstrate surprisingly impressive perf...
Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovi...
CSC
2006
13 years 11 months ago
Applying Sparse Matrix Solvers to a Glacial Ice Sheet Model
- Two software packages for solving sparse systems of linear equations, SuperLU and UMFPACK, have been integrated with the University of Maine Ice Sheet Model for predicting the fo...
Rodney Jacobs, James Fastook, Aitbala Sargent
EWC
2011
52views more  EWC 2011»
13 years 4 months ago
Localized coarsening of conforming all-hexahedral meshes
Abstract. Finite element mesh adaptation methods can be used to improve the efficiency and accuracy of solutions to computational modeling problems. In many applications involving ...
Adam C. Woodbury, Jason F. Shepherd, Matthew L. St...
PODS
2008
ACM
153views Database» more  PODS 2008»
14 years 10 months ago
Approximation algorithms for co-clustering
Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by severa...
Aris Anagnostopoulos, Anirban Dasgupta, Ravi Kumar