We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slon...
How does one extract unknown but stereotypical events that are linearly superimposed within a signal with variable latencies and variable amplitudes? One could think of using temp...
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
Proper data placement schemes based on erasure correcting code are one of the most important components for a highly available data storage system. For such schemes, low decoding ...