重新思考隐式神经表示的非负矩阵分解

Apr, 2024

重新思考隐式神经表示的非负矩阵分解

Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

Krishna Subramani, Paris Smaragdis, Takuya Higuchi, Mehrez Souden

TL;DR非负矩阵分解 (Non-negative Matrix Factorization, NMF) 是一种强大的用于分析规则采样数据的技术，本文将 NMF 表述为连续函数的形式，并展示 NMF 可以扩展到更多不需要规则采样的信号类别。

Abstract

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-frequen