Blind Source Separation Algorithm: Non-negative Matrix Factorization (NMF)

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Resource Overview

A novel theory in blind source separation algorithms - Non-negative Matrix Factorization (NMF). The package includes implementation code with a graphical user interface (GUI) for practical applications.

Detailed Documentation

In blind source separation algorithms, Non-negative Matrix Factorization (NMF) represents an emerging theoretical approach based on decomposing a non-negative matrix into the product of two non-negative matrices, where all elements maintain non-negative values. The algorithm implementation typically involves optimization techniques like multiplicative update rules or gradient descent to minimize reconstruction error. NMF has found extensive applications in signal processing, image analysis, and speech recognition domains. The provided package contains complete MATLAB/Python implementation code featuring key functions for matrix initialization, factor updates, and convergence checking, along with an intuitive GUI interface that allows users to visualize decomposition results and adjust parameters interactively for experimental use.

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