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Found 33 papers, 19 papers with code
Majorization-minimization for Sparse Nonnegative Matrix Factorization with the $β$-divergence
It is well known that the norm of the other factor (the dictionary matrix) needs to be controlled in order to avoid an ill-posed formulation.
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain.
A majorization-minimization algorithm for nonnegative binary matrix factorization
We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model's expressive power.
Accelerating Non-Negative and Bounded-Variable Linear Regression Algorithms with Safe Screening
Non-negative and bounded-variable linear regression problems arise in a variety of applications in machine learning and signal processing.
Leveraging Joint-Diagonalization in Transform-Learning NMF
Non-negative matrix factorization with transform learning (TL-NMF) is a recent idea that aims at learning data representations suited to NMF.
Joint Majorization-Minimization for Nonnegative Matrix Factorization with the $β$-divergence
Our new updates are derived from a joint majorization-minimization (MM) scheme, in which an auxiliary function (a tight upper bound of the objective function) is built for the two factors jointly and minimized at each iteration.
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
In particular, we consider majorization-minimization which leads in our setting to efficient multiplicative updates for a variety of penalties.
Adversarially-Trained Nonnegative Matrix Factorization
We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique.
Second-order step-size tuning of SGD for non-convex optimization
In view of a direct and simple improvement of vanilla SGD, this paper presents a fine-tuning of its step-sizes in the mini-batch case.
Neural content-aware collaborative filtering for cold-start music recommendation
In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart.
Expanding boundaries of Gap Safe screening
In this work, we extend the existing Gap Safe screening framework by relaxing the global strong-concavity assumption on the dual cost function.
Leveraging the structure of musical preference in content-aware music recommendation
These approaches are agnostic to the song content, and therefore face the cold-start problem: they cannot recommend novel songs without listening history.
Phase retrieval with Bregman divergences and application to audio signal recovery
Therefore, we formulate PR as a new minimization problem involving Bregman divergences.
Sound
Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank Minimization
In this work, we provide a collection of tools for PSDMF, by showing that PSDMF algorithms can be designed based on phase retrieval (PR) and affine rank minimization (ARM) algorithms.
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing
We show on numerical experiments that the MU are able to obtain high resolutions in both dimensions on two applications: (1) blind unmixing of audio spectrograms: to the best of our knowledge, this is the first time a coupled NMF model is used in this context, and (2) the fusion of hyperspectral and multispectral images: we show that the MU compete favorable with state-of-the-art algorithms in particular in the presence of non-Gaussian noise.
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data.
Ordinal Non-negative Matrix Factorization for Recommendation
In particular, our algorithm preserves the scalability of PF and can be applied to huge sparse datasets.
An Inertial Newton Algorithm for Deep Learning
We prove the convergence of INNA for most deep learning problems.
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user.
A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments
We propose a novel ranking model that combines the Bradley-Terry-Luce probability model with a nonnegative matrix factorization framework to model and uncover the presence of latent variables that influence the performance of top tennis players.
Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization
In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices.
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing.
Factor analysis of dynamic PET images: beyond Gaussian noise
Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data.
Estimation with Low-Rank Time-Frequency Synthesis Models
In this paper we instead propose a synthesis approach, where low-rankness is imposed to the synthesis coefficients of the data signal over a given t-f dictionary (such as a Gabor frame).
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data.
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.
Nonnegative Matrix Factorization with Transform Learning
Traditional NMF-based signal decomposition relies on the factorization of spectral data, which is typically computed by means of short-time frequency transform.
Optimal spectral transportation with application to music transcription
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.
Low-Rank Time-Frequency Synthesis
Many single-channel signal decomposition techniques rely on a low-rank factorization of a time-frequency transform.
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
This paper introduces a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures.
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation
In this paper we describe a maximum likelihood likelihood approach for dictionary learning in the multiplicative exponential noise model.
Automatic Relevance Determination in Nonnegative Matrix Factorization with the β-Divergence
This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the \beta-divergence.
Algorithms for nonnegative matrix factorization with the beta-divergence
The paper also describes how the proposed algorithms can be adapted to two common variants of NMF : penalized NMF (i. e., when a penalty function of the factors is added to the criterion function) and convex-NMF (when the dictionary is assumed to belong to a known subspace).
