Results 41 to 50 of about 112,822 (316)
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
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Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization
Cybernetics and Information Technologies, 2014 In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
doaj +1 more source
Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
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Robust Adaptive Graph Regularized Non-Negative Matrix Factorization
IEEE Access, 2019 Data clustering, which aims to divide the given samples into several different groups, has drawn much attention in recent years. As a powerful tool, non-negative matrix factorization (NMF) has been applied successfully in clustering tasks. However, there
Xiang He, Qi Wang, Xuelong Li
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MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization. [PDF]
PLoS ONE, 2015 In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across ...
Olivier Mangin +3 more
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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 +3 more sources
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
On Rank Selection in Non-Negative Matrix Factorization Using Concordance
Mathematics, 2023 The choice of the factorization rank of a matrix is critical, e.g., in dimensionality reduction, filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to adjusting the noise, while selecting a rank that is too low ...
Paul Fogel +3 more
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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
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