Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...
Gift Nyamundanda, Lorraine Brennan, Isobel Claire ...
Treatment of general structured information by neural networks is an emerging research topic. Here we show how representations for graphs preserving all the information can be devi...
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. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...