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Nonnegative matrix factorization with matrix exponentiation
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Nonnegative 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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2019
Many data generating processes result in skewed data, which should be modeled by distributions that can capture the skewness. In this work we adopt the flexible family of Lambert W distributions that combine arbitrary standard distribution with specific nonlinear transformation to incorporate skewness.
Arto Klami, Jarkko Lagus, Joseph Sakaya
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Many data generating processes result in skewed data, which should be modeled by distributions that can capture the skewness. In this work we adopt the flexible family of Lambert W distributions that combine arbitrary standard distribution with specific nonlinear transformation to incorporate skewness.
Arto Klami, Jarkko Lagus, Joseph Sakaya
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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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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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Quadratic nonnegative matrix factorization
Pattern Recognition, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Zhirong, Oja, Erkki
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Deep Bayesian Matrix Factorization
2017Matrix 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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Deep Non-Negative Matrix Factorization Architecture Based on Underlying Basis Images Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Yang Zhao, Huiyang Wang, Jihong Pei
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Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Deng Cai, Xiaofei He, Jiawei Han
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Matrix Factorization Techniques for Recommender Systems
Computer, 2009Yehuda Koren, Chris Volinsky
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Constrained Nonnegative Matrix Factorization for Image Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012Haifeng Liu, Deng Cai, Xuelong Li
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