Feature extraction using random matrix theory approach

Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. In this paper, we propose to broaden the feature extraction algorithms with Random Matrix Theory methodology. Testing the cross-correlation matrix of variables against the null hypothesis of random correlations, we can derive characteristic parameters of the system, such as boundaries of eigenvalue spectra of random correlations, distribution of eigenvalues and eigenvectors of random correlations, inverse participation ratio and stability of eigenvectors of non-random correlations. We demonstrate the usefullness of these parameters for network traffic application, in particular, for network congestion control and for detection of any changes in the stable traffic dynamics.