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 …
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 …
Sketch-then-edit generative adversarial network
Abstract Generative Adversarial Network (GAN) has been widely used to generate
impressively plausible data. However, it is a non-trivial task to train the original GAN model …
impressively plausible data. However, it is a non-trivial task to train the original GAN model …
Flexible and hierarchical prior for Bayesian nonnegative matrix factorization
J Lu, X Ye - arXiv preprint arXiv:2205.11025, 2022 - arxiv.org
In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization
(NMF) that is commonly used for predicting missing values and finding hidden patterns in …
(NMF) that is commonly used for predicting missing values and finding hidden patterns in …
Bayesian low-rank interpolative decomposition for complex datasets
J Lu - arXiv preprint arXiv:2205.14825, 2022 - arxiv.org
In this paper, we introduce a probabilistic model for learning interpolative decomposition
(ID), which is commonly used for feature selection, low-rank approximation, and identifying …
(ID), which is commonly used for feature selection, low-rank approximation, and identifying …
Robust Bayesian nonnegative matrix factorization with implicit regularizers
J Lu, CP Chai - arXiv preprint arXiv:2208.10053, 2022 - arxiv.org
We introduce a probabilistic model with implicit norm regularization for learning nonnegative
matrix factorization (NMF) that is commonly used for predicting missing values and finding …
matrix factorization (NMF) that is commonly used for predicting missing values and finding …
Comparative study of inference methods for interpolative decomposition
J Lu - arXiv preprint arXiv:2206.14542, 2022 - arxiv.org
In this paper, we propose a probabilistic model with automatic relevance determination
(ARD) for learning interpolative decomposition (ID), which is commonly used for low-rank …
(ARD) for learning interpolative decomposition (ID), which is commonly used for low-rank …
Bayesian Matrix Decomposition and Applications
J Lu - arXiv preprint arXiv:2302.11337, 2023 - arxiv.org
The sole aim of this book is to give a self-contained introduction to concepts and
mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix …
mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix …
Improving the robustness of wasserstein embedding by adversarial PAC-Bayesian learning
Node embedding is a crucial task in graph analysis. Recently, several methods are
proposed to embed a node as a distribution rather than a vector to capture more information …
proposed to embed a node as a distribution rather than a vector to capture more information …
Prior specification for Bayesian matrix factorization via prior predictive matching
The behavior of many Bayesian models used in machine learning critically depends on the
choice of prior distributions, controlled by some hyperparameters typically selected through …
choice of prior distributions, controlled by some hyperparameters typically selected through …