Results 51 to 60 of about 135,346 (336)
An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization [PDF]
, 2017 The aim of this paper is to provide some theoretical understanding of quasi-Bayesian aggregation methods non-negative matrix factorization. We derive an oracle inequality for an aggregated estimator.
Alquier, Pierre, Guedj, Benjamin
core +5 more sources
The Web Conference, 2018 Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia.
Tian Shi +3 more
semanticscholar +1 more source
Enforced Sparse Non-negative Matrix Factorization [PDF]
2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2016 Non-negative matrix factorization (NMF) is a common method for generating topic models from text data. NMF is widely accepted for producing good results despite its relative simplicity of implementation and ease of computation. One challenge with applying NMF to large datasets is that intermediate matrix products often become dense, stressing the ...
Jeremy Kepner +2 more
openaire +4 more sources
Sample Complexity of Dictionary Learning and other Matrix Factorizations [PDF]
, 2015 Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by ...
Bach, Francis +4 more
core +5 more sources
Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization
IEEE Access, 2021 Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of the primary objectives of text mining. One of the big challenges in topic modeling is determining the correct number of topics: underestimating the number ...
Raviteja Vangara +8 more
semanticscholar +1 more source
On the Identifiability of Transform Learning for Non-Negative Matrix Factorization [PDF]
IEEE Signal Processing Letters, 2020 Non-negative matrix factorization with transform learning (TL-NMF) aims at estimating a short-time orthogonal transform that projects temporal data into a domain that is more amenable to NMF than off-the-shelf time-frequency transforms. In this work, we study the identifiability of TL-NMF under the Gaussian composite model.
Sixin Zhang +2 more
openaire +4 more sources
Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification. [PDF]
PLoS ONE, 2015 Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they
Xiang Zhang +4 more
doaj +1 more source
Non-negative Matrix Factorization
Definitions, 2020 Linear dimensionality reduction techniques such as principal component analysis and singular value decomposition are powerful tools for dealing with high dimensional data. In this report, we will explore a linear dimensionality reduction technique namely
Morten Mørup +3 more
semanticscholar +1 more source
Population Ecology, EarlyView.This study examines the demographic dynamics of two seabird populations on Tromelin Island, 15 years after the eradication of brown rats. The results indicate that these populations are in good health and are expected to continue growing until breeding sites are saturated in about a century.
Merlène Saunier +6 more
wiley +1 more source
A deep matrix factorization method for learning attribute representations [PDF]
, 2015 Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix
Bousmalis, Konstantinos +3 more
core +3 more sources