Results 241 to 250 of about 207,171 (287)

Exploring patterns of multimorbidity in South Korea using exploratory factor analysis and non negative matrix factorization. [PDF]

Sci Rep
Kim Y, Park S, Choi YM, Yoon BH, Kim SH, Park J, Oh HJ, Lim Y, Lee J, Park B.   +9 more
europepmc   +1 more source

CellPie: a scalable spatial transcriptomics factor discovery method via joint non-negative matrix factorization. [PDF]

Nucleic Acids Res
Georgaka S, Morgans WG, Zhao Q, Martinez DS, Ali A, Ghafoor M, Baker SM, Bristow RG, Iqbal M, Rattray M.   +9 more
europepmc   +1 more source

Accountability Assessment of Source-Specific Impacts of Regulations on Emissions and Air Quality Using Positive Matrix Factorization. [PDF]

Environ Sci Technol
Gao Z, Mei EJ, He X, Ebelt S, Rich DQ, Russell AG.   +5 more
europepmc   +1 more source

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Ternary Matrix Factorization

2014 IEEE International Conference on Data Mining, 2014
Can we learn from the unknown? Logical data sets of the ternary kind are often found in information systems. They contain unknown as well as true/false values. An unknown value may represent a missing entry (lost or indeterminable) or something with meaning, like a 'Don't Know' response in a questionnaire. In this paper we introduce an effectively- and
Maurus, S., Plant, C.
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Matrix completion by deep matrix factorization

Neural Networks, 2018
Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations.
Jicong Fan, Jieyu Cheng
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Factor-Bounded Nonnegative Matrix Factorization

ACM Transactions on Knowledge Discovery from Data, 2021
Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems.
Kai Liu, Xiangyu Li, Zhihui Zhu, Lodewijk Brand, Hua Wang   +4 more
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Quadratic nonnegative matrix factorization

Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Zhirong, Oja, Erkki
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Nonnegative matrix factorization with matrix exponentiation

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Nonnegative matrix factorization (NMF) has been successfully applied to different domains as a technique able to find part-based linear representations for nonnegative data. However, when extra constraints are incorporated into NMF, simple gradient descent optimization can be inefficient for high-dimensional problems, due to the overhead to enforce the
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Deep Bayesian Matrix Factorization

2017
Matrix factorization is a popular collaborative filtering technique, assuming that the matrix of ratings can be written as the inner product of two low-rank matrices, comprising latent features assigned to each user/item. Recently, several researchers have developed Bayesian treatments of matrix factorization, that infer posterior distributions over ...
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Matrix forgetting factor

International Journal of Systems Science, 1999
This study suggests a new approach to provide time-varying parameter estimates in ARMA (Auto Regression Moving Average) models of stochastic nature based on the use of the recursive version of Instrumental Variable Method (IVM) with a Matrix Forgetting Factor (MFF).
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