A random version of principal component analysis in data clustering
Sign Up to like & getrecommendations! Published in 2018 at "Computational biology and chemistry"
DOI: 10.1016/j.compbiolchem.2018.01.009
Abstract: Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance/correlation matrix of the analyzed data. However, to properly work with high-dimensional data sets, PCA poses severe mathematical constraints on… read more here.
Keywords: principal component; analysis; correlation matrix; data clustering ... See more keywords
Covariance and correlation estimators in bipartite complex systems with a double heterogeneity
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Mechanics: Theory and Experiment"
DOI: 10.1088/1742-5468/ab16c5
Abstract: We present a weighted estimator of the covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators lies in the fact that the unweighted sample… read more here.
Keywords: bipartite complex; complex systems; systems double; covariance correlation ... See more keywords