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When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
Non-Negative Matrix Factorization Parts
2015/8/21
We interpret non-negative matrix factorization geometrically, as the
problem of finding a simplicial cone which contains a cloud of data
points and which is contained in the positive orthant.
Grouping tasks and data display items via the non-negative matrix factorization
Grouping tasks non-negative matrix factorization
2015/8/21
Analyzing work functions and the IO variables they
need is an important component of designing and evaluating
complex systems. We develop a biclustering method for jointly
grouping work functions a...
Accelerated Multiplicative Updates and Hierarchical ALS Algorithms for Nonnegative Matrix Factorization
nonnegative matrix factorization algorithms multiplicative updates hierarchical alternating least squares
2011/9/21
Abstract: Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of applications such as text mining, image processing, hyperspectral data analysis, computational ...
Divide-and-Conquer Matrix Factorization
Divide-and-Conquer Matrix Factorization Numerical Analysis
2011/9/29
Abstract: This work introduces SubMF, a parallel divide-and-conquer framework for noisy matrix factorization. SubMF divides a large-scale matrix factorization task into smaller subproblems, solves eac...
Matrix Factorization for an SO(2) Spinning Top and Related Problems
Matrix Factorization SO(2) Spinning Top Related Problems
2010/11/1
We study the matrix factorization problem associated with an SO(2) spinning top by using the algebro-geometric approach. We derive the explicit expressions in terms of Riemann theta functions and dis...
A Multilevel Approach For Nonnegative Matrix Factorization
nonnegative matrix factorization algorithms multigrid and multilevel methods
2010/11/30
Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative ma-trix with the product of two low-rank nonnegative matrices and has been shown to be particularly
useful in many...