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Found 9 papers, 5 papers with code
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
However, from a practical perspective, the choice of regularizers and regularization coefficients, as well as the design of efficient algorithms, is challenging because of the multifactor nature of these models and the lack of theory to back these choices.
Block Majorization Minimization with Extrapolation and Application to $β$-NMF
We propose a Block Majorization Minimization method with Extrapolation (BMMe) for solving a class of multi-convex optimization problems.
Deep Nonnegative Matrix Factorization with Beta Divergences
Deep Nonnegative Matrix Factorization (deep NMF) has recently emerged as a valuable technique for extracting multiple layers of features across different scales.
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms.
Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals
Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval.
Multiplicative Updates for NMF with $β$-Divergences under Disjoint Equality Constraints
In this paper, we introduce a general framework to design multiplicative updates (MU) for NMF based on $\beta$-divergences ($\beta$-NMF) with disjoint equality constraints, and with penalty terms in the objective function.
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.
Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF
Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider on this paper the blind audio source separation problem which consists in isolating and extracting each of the sources.
Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization
We propose to use Lagrange duality to judiciously optimize for a set of weights to be used within the framework of the weighted-sum approach, that is, we minimize a single objective function which is a weighted sum of the all objective functions.
