Kevin Scott
Principal Research Statistician

Kevin Scott is a principal research statistician developer at SAS Institute Inc. He received a Master’s in statistics from North Carolina State University. He has worked at SAS for over 25 years and has developed analytical solutions for the manufacturing, financial services, pharmaceuticals, retail, hospitality and travel industries.

Advanced Analytics | Machine Learning
Kevin Scott 0
SAS® Fast-KPCA: An efficient and innovative nonlinear principal components method

SAS® Fast-KPCA implementation bypasses the limitations of exact KPCA methods. SAS® internally uses k-means to find a representative sample of a subset of points. This row reduction method has the advantage that c centroids are chosen to minimize the variation of points nearest to each centroid and maximize the variation to the other cluster centroids. In some cases, the downstream effect of using k-means on computing the SVD increases numerical stability and improves clustering, discrimination, and classification.