Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

2024-04-05 · Krishna Subramani, Paris Smaragdis, Takuya Higuchi, Mehrez Souden

Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency (TF) representations like the Short-Time Fourier Transform. However extending these applications to irregularly-spaced TF representations, like the Constant-Q transform, wavelets, or sinusoidal analysis models, has not been possible since these representations cannot be directly stored in matrix form. In this paper, we formulate NMF in terms of continuous functions (instead of fixed vectors) and show that NMF can be extended to a wider variety of signal classes that need not be regularly sampled.

📄 PDF Abstract BibTeX arXiv:2404.04439

Code (1)

subramanikrishna/in-nmf 공식 구현 pytorch

Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective

2022-09-06 · Mengyuan Zhang, Kai Liu

Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability. Among general NMF problems, symmetric NMF is a special one that plays an important role in graph clustering where each el…

Clustering Graph Clustering

A deep matrix factorization method for learning attribute representations

2015-09-10 · George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern W. Schuller

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new represent…

Attribute Clustering

Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All

2021-09-13 · ACM Conference on Recommender Systems 2021 9 · Florian Wilhelm

Matrix factorization-based methods are among the most popular methods for collaborative filtering tasks with implicit feedback. The most effective of these methods do not apply sign constraints, such as non-negativity, t…

All Collaborative Filtering

Implicit Regularization in Deep Learning May Not Be Explainable by Norms

2020-05-13 · NeurIPS 2020 12 · Noam Razin, Nadav Cohen

Mathematically characterizing the implicit regularization induced by gradient-based optimization is a longstanding pursuit in the theory of deep learning. A widespread hope is that a characterization based on minimizatio…

Deep Learning Matrix Completion Open-Ended Question Answering

Ordinal Non-negative Matrix Factorization for Recommendation

2020-06-01 · ICML 2020 1 · Olivier Gouvert, Thomas Oberlin, Cédric Févotte

We introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the categories. In particular, they can be fou…

Binarization Recommendation Systems