Log-based sparse nonnegative matrix factorization for data representation
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its
effectiveness in representing nonnegative data with parts-based representations. For NMF …
effectiveness in representing nonnegative data with parts-based representations. For NMF …
Rectified Gaussian scale mixtures and the sparse non-negative least squares problem
In this paper, we develop a Bayesian evidence maximization framework to solve the sparse
non-negative least squares (S-NNLS) problem. We introduce a family of probability densities …
non-negative least squares (S-NNLS) problem. We introduce a family of probability densities …
Dictionaries in machine learning
Abstract Machine learning and artificial intelligence are domains that have long been
influenced by and, in turn, have influenced our understanding of how effective and efficient …
influenced by and, in turn, have influenced our understanding of how effective and efficient …
Image denoising neural network architecture and method of training the same
US10726525B2 - Image denoising neural network architecture and method of training the same
- Google Patents US10726525B2 - Image denoising neural network architecture and method …
- Google Patents US10726525B2 - Image denoising neural network architecture and method …
Multimodal sparse bayesian dictionary learning
This paper addresses the problem of learning dictionaries for multimodal datasets, ie
datasets collected from multiple data sources. We present an algorithm called multimodal …
datasets collected from multiple data sources. We present an algorithm called multimodal …
Nonnegative sparse representation of signals with a determinant-type sparsity measure based on the dc programming
Nonnegative sparse representation has become highly popular in certain applications in the
context of signals and corresponding dictionaries that have nonnegative limitations …
context of signals and corresponding dictionaries that have nonnegative limitations …
[HTML][HTML] A novel framework for the NMF methods with experiments to unmixing signals and feature representation
Non-negative matrix factorization (NMF) can be used in clustering, feature representation or
blind source separation. Many NMF methods have been developed including least squares …
blind source separation. Many NMF methods have been developed including least squares …
[LIVRE][B] Structured Learning with Scale Mixture Priors
I Fedorov - 2018 - search.proquest.com
Sparsity plays an essential role in a number of modern algorithms. This thesis examines
how we can incorporate additional structural information within the sparsity profile and …
how we can incorporate additional structural information within the sparsity profile and …
[PDF][PDF] Non-Negative Matrix Factorization Meets Time-Inhomogeneous Markov Chains
Non-negative matrix factorization (NMF)[22] is a popular unsupervised learning approach
that allows to obtain part-based representations of non-negative data samples and provide …
that allows to obtain part-based representations of non-negative data samples and provide …
Robustness verification and novelty detection of deep learning-based raman spectral Analysis
Z Lei - 2022 - era.ed.ac.uk
Sparse coding aims at forming a linear combination of a few basis atoms out of a large set,
to reconstruct input vectors. Conventional model-based methods often tend to be slow and …
to reconstruct input vectors. Conventional model-based methods often tend to be slow and …