STRONG: metagenomics strain resolution on assembly graphs
Abstract We introduce STrain Resolution ON assembly Graphs (STRONG), which identifies
strains de novo, from multiple metagenome samples. STRONG performs coassembly, and …
strains de novo, from multiple metagenome samples. STRONG performs coassembly, and …
Learning nonnegative factors from tensor data: Probabilistic modeling and inference algorithm
Tensor canonical polyadic decomposition (CPD) with nonnegative factor matrices, which
extracts useful latent information from multidimensional data, has found wide-spread …
extracts useful latent information from multidimensional data, has found wide-spread …
Low-rank nonnegative matrix factorization on Stiefel manifold
P He, X Xu, J Ding, B Fan - Information Sciences, 2020 - Elsevier
Low rank is an important but ill-posed problem in the development of nonnegative matrix
factorization (NMF) algorithms because the essential information is often encoded in a low …
factorization (NMF) algorithms because the essential information is often encoded in a low …
Bayesian non-negative matrix factorization with Student's t-distribution for outlier removal and data clustering
R Yuan, C Leng, S Zhang, J Peng, A Basu - Engineering Applications of …, 2024 - Elsevier
Abstract Non-negative Matrix Factorization (NMF) is an effective way to solve the
redundancy of non-negative high-dimensional data. Most of the traditional probability-based …
redundancy of non-negative high-dimensional data. Most of the traditional probability-based …
Variational bayesian orthogonal nonnegative matrix factorization over the stiefel manifold
Nonnegative matrix factorization (NMF) is one of the best-known multivariate data analysis
techniques. The NMF uniqueness and its rank selection are two major open problems in this …
techniques. The NMF uniqueness and its rank selection are two major open problems in this …
β-divergence NMF with biorthogonal regularization for data representation
Abstract Non-Negative Matrix Factorization (NMF) has become a commonly used method for
data representation. Orthogonal NMF improves the clustering performance by adding …
data representation. Orthogonal NMF improves the clustering performance by adding …
Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior
Non-negative matrix factorization (NMF) is generally an ill-posed problem which requires
further regularization. Regularization of NMF using the assumption of sparsity is common as …
further regularization. Regularization of NMF using the assumption of sparsity is common as …
Bayesian nonnegative matrix factorization in an incremental manner for data representation
Nonnegative matrix factorization (NMF) is a novel paradigm for feature representation and
dimensionality reduction. However, the performance of the NMF model is affected by two …
dimensionality reduction. However, the performance of the NMF model is affected by two …
A variational Bayesian Gaussian mixture-nonnegative matrix factorization model to extract movement primitives for robust control
H Xie, K Mengersen, C Di, Y Zhang… - … on Human-Machine …, 2022 - ieeexplore.ieee.org
Nonnegative matrix factorization (NMF) is a powerful tool for parameter estimation applied in
numerous robotics applications, such as path planning, motion trajectory prediction, and …
numerous robotics applications, such as path planning, motion trajectory prediction, and …
Probabilistic sparse non-negative matrix factorization
JL Hinrich, M Mørup - Latent Variable Analysis and Signal Separation …, 2018 - Springer
In this paper, we propose a probabilistic sparse non-negative matrix factorization model that
extends a recently proposed variational Bayesian non-negative matrix factorization model to …
extends a recently proposed variational Bayesian non-negative matrix factorization model to …