Spectral signal unmixing with interior-point nonnegative matrix factorization

Authors:
Rafal Zdunek
Affiliations:
Institute of Telecommunications, Teleinformatics and Acoustics, Wroclaw University of Technology, Wroclaw, Poland
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 6
Cited 0

Non-negative Matrix Factorization with Orthogonality Constraints and its Application to Raman Spectroscopy

Journal of VLSI Signal Processing Systems
Projected Gradient Methods for Nonnegative Matrix Factorization

Neural Computation
Nonnegative matrix factorization with quadratic programming

Neurocomputing
Non-negatively constrained image deblurring with an inexact interior point method

Journal of Computational and Applied Mathematics
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization

IEEE Transactions on Image Processing

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Abstract

Nonnegative Matrix Factorization (NMF) is an unsupervised learning method that has been already applied to many applications of spectral signal unmixing. However, its efficiency in some applications strongly depends on optimization algorithms used for estimating the underlying nonnegatively constrained subproblems. In this paper, we attempt to tackle the optimization tasks with the inexact Interior-Point (IP) algorithm that has been successfully applied to image deblurring [S. Bonettini, T. Serafini, 2009]. The experiments demonstrate that the proposed NMF algorithm considerably outperforms the well-known NMF algorithms for blind unmixing of the Raman spectra.