Title : 
Sparse component analysis and blind source separation of underdetermined mixtures
         
        
            Author : 
Georgiev, Pando ; Theis, Fabian ; Cichocki, Andrzej
         
        
            Author_Institution : 
Dept. of Electr. Comput. & Eng. Comput. Sci., Univ. of Cincinnati, OH, USA
         
        
        
        
        
            fDate : 
7/1/2005 12:00:00 AM
         
        
        
        
            Abstract : 
In this letter, we solve the problem of identifying matrices S ∈ Rn×N and A ∈ Rm×n knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.
         
        
            Keywords : 
blind source separation; identification; sparse matrices; blind source separation; identifiability condition; matrix identification; sparse component analysis; underdetermined mixtures; Blind source separation; Data analysis; Data mining; Dictionaries; Independent component analysis; Neuroscience; Random variables; Signal processing algorithms; Source separation; Sparse matrices; Blind source separation (BSS); sparse component analysis (SCA); underdetermined mixtures; Algorithms; Artificial Intelligence; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated;
         
        
        
            Journal_Title : 
Neural Networks, IEEE Transactions on
         
        
        
        
        
            DOI : 
10.1109/TNN.2005.849840