Big Data

High Performance Multidimensional Scaling for Large High-Dimensional Data Visualization

Date Added: Jan 2012
Format: PDF

Technical advancements produces a huge amount of scientific data which are usually in high dimensional formats, and it is getting more important to analyze those large-scale high-dimensional data. Dimension reduction is a well-known approach for high-dimensional data visualization, but can be very time and memory demanding for large problems. Among many dimension reduction methods, multidimensional scaling does not require explicit vector representation but uses pair-wise dissimilarities of data, so that it has a broader applicability than the other algorithms which can handle only vector representation.