Add to ORKGInferential, robust non-negative matrix factorization analysis of microarray dat
Abstract: In the last decade, advances in high-through-put technologies such as DNA microarrays have...
jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics dat
© 2016 ACM. Supervised feature extraction methods have received considerable attention in the data m...
Nonnegative Matrix Factorization (NMF) is a class of low-rank dimensionality reduction methods whic...
Nonnegative Matrix Factorization (NMF) is a class of low-rank dimensionality reduction methods whic...
ABSTRACT Over the past few years, there has been a considerable spread of microarray technology in ...
Abstract Background Non-negative matrix factorisation (NMF), a machine learning algorithm, has been ...
A non-negative matrix factorization framework for identifying modular patterns in metagenomic profil...
Nonnegative Matrix Factorization (NMF) revealed to be useful for analysing microarray data and extr...
Exploiting sample variability to enhance multivariate analysis of microarray dat
This edited book collects new results, concepts and further developments of NMF. The open problems d...
Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. ...
Many practical pattern recognition problems require non-negativity constraints. For example, pixels ...
This book collects new results, concepts and further developments of NMF. The open problems discusse...
A unified approach to non-negative matrix factorization and probabilistic latent semantic indexin
Abstract: In the last decade, advances in high-through-put technologies such as DNA microarrays have...
jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics dat
© 2016 ACM. Supervised feature extraction methods have received considerable attention in the data m...
Nonnegative Matrix Factorization (NMF) is a class of low-rank dimensionality reduction methods whic...
Nonnegative Matrix Factorization (NMF) is a class of low-rank dimensionality reduction methods whic...
ABSTRACT Over the past few years, there has been a considerable spread of microarray technology in ...
Abstract Background Non-negative matrix factorisation (NMF), a machine learning algorithm, has been ...
A non-negative matrix factorization framework for identifying modular patterns in metagenomic profil...
Nonnegative Matrix Factorization (NMF) revealed to be useful for analysing microarray data and extr...
Exploiting sample variability to enhance multivariate analysis of microarray dat
This edited book collects new results, concepts and further developments of NMF. The open problems d...
Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. ...
Many practical pattern recognition problems require non-negativity constraints. For example, pixels ...
This book collects new results, concepts and further developments of NMF. The open problems discusse...
A unified approach to non-negative matrix factorization and probabilistic latent semantic indexin
Abstract: In the last decade, advances in high-through-put technologies such as DNA microarrays have...
jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics dat
© 2016 ACM. Supervised feature extraction methods have received considerable attention in the data m...