|
|
|
|
|
Paola Favati, Grazia Lotti, Ornella Menchi and Francesco Romani
The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a sim...
ver más
|
|
|
|
|
|
|
|
|
|
|
Michael R. Lindstrom, Xiaofu Ding, Feng Liu, Anand Somayajula and Deanna Needell
Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain...
ver más
|
|
|
|
|
|
|
|
|
|
|
Rui-Yu Li, Yu Guo and Bin Zhang
Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized extens...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jingjing Cao, Li Zhuo and Haiyan Tao
|
|
|
|
|
|
|
|
|
|
|
Ali Dehghan Firoozabadi, Pablo Irarrazaval, Pablo Adasme, David Zabala-Blanco, Hugo Durney, Miguel Sanhueza, Pablo Palacios-Játiva and Cesar Azurdia-Meza
Speech enhancement is one of the most important fields in audio and speech signal processing. The speech enhancement methods are divided into the single and multi-channel algorithms. The multi-channel methods increase the speech enhancement performance b...
ver más
|
|
|
|
|
|