In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combi...
Abstract—Large-scale parallel applications often produce immense quantities of data that need to be analyzed. To avoid performing repeated, costly disk accesses, analysis of larg...
: With the ever-increasing demands on server applications, reliability is of paramount importance. Often these services are implemented using a distributed server cluster architect...
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Dynamic events can be regarded as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple st...