Negative Binomial Matrix Completion
Matrix completion focuses on recovering missing or incomplete information in matrices. This problem arises in various applications, including image processing and network analysis. Previous research proposed Poisson matrix completion for count data with noise that follows a Poisson distribution, which assumes that the mean and variance are equal. Since overdispersed count data, whose variance is greater than the mean, is more likely to occur in realistic settings, we assume that the noise follows the negative binomial (NB) distribution, which can be more general than the Poisson distribution. In this paper, we introduce NB matrix completion by proposing a nuclear-norm regularized model that can be solved by proximal gradient descent. In our experiments, we demonstrate that the NB model outperforms Poisson matrix completion in various noise and missing data settings on real data.
Code (0)
등록된 구현이 없습니다.
Tasks
Matrix CompletionSimilar Papers 제목 키워드 기반
Negative Binomial Matrix Factorization for Recommender Systems
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson matrix factorization (P…
BinarizationRecommendation SystemsNonparametric Bayesian Negative Binomial Factor Analysis
A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood. We show its limitation in capt…
Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes
We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns. The three distributions we consider are derived from the gamma-Poisson, gamma-negative bi…
feature selectiontext-classificationText ClassificationClustering of Nonnegative Data and an Application to Matrix Completion
In this paper, we propose a simple algorithm to cluster nonnegative data lying in disjoint subspaces. We analyze its performance in relation to a certain measure of correlation between said subspaces. We use our clusteri…
ClusteringMatrix CompletionRelationMM-algorithms for traditional and convex NMF with Tweedie and Negative Binomial cost functions and empirical evaluation
Non-negative matrix factorisation (NMF) is a widely used tool for unsupervised learning and feature extraction, with applications ranging from genomics to text analysis and signal processing. Standard formulations of NMF…