Approximate non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in biomedical data analysis. In this paper, we proposed a new NMF algorithm with temporal smoothness constraint that aims to extract non-negative components that have meaningful physical or physiological interpretations. We propose two constraints and derive new multiplicative learning rules. Specifically, we apply the proposed algorithm, combined with advanced time-frequency analysis and machine learning techniques, to early detection of Alzheimer disease using clinical EEG recordings. Empirical results show promising performance.
|Title of host publication||2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings|
|Publication status||Published - 2006|
|Event||2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France|
Duration: 14 May 2006 → 19 May 2006
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006|
|Period||14/05/06 → 19/05/06|