Blind sources separation based on non-negative matrix factorization
Le Wei · Electronics Optics & Control · 2004
Independent Component Analysis (ICA) is a widely applicable and effective approach in Blind Source Separation (BSS) with the limitation that the sources are independent to each other, while more commonly situation is blind source separation where sources are statistically dependent to each other. In this paper, a novel idea for blind source separation is presented that the blind independent source separation is essentially matrix factorization to factorize the observation matrix into a mixing matrix and a source matrix where the sources are independent to each other, while blind non-negative dependent source separation is essentially a matrix factorization to factorize the observation matrix into a non-negative mixing matrix and a non-negative source matrix where the sources can be dependent to each other. Non-negative Matrix Factorization (NMF) technique is applied to this non-negative dependent source separation and proved by computer simulations and by Partial Volume Correction (PVC) experiments for real microarray data that it is of great effectiveness when the sources are dependent to each other and/or Gaussian distributed.