Forward Projection for High-Dimensional Data

By Thomas Ortner, Peter Filzmoser, Sarka Brodinova, Maia Zaharieva, and Christian Breiteneder

Abstract

We provide a novel view on group structure in data. Projecting observations onto a subspace spanned by a small selection of observations, we calculate orthogonal distances as a measure for dissimilarity. Sequentially exchanging the observations, used to span the subspace, we receive a series of distances. Observations, taken from a similar group structure will behave similar along those projections. This leads to a visualisation of high dimensional data providing some basic diagnostic on group structures and outliers. The series of distances can be further utilized to perform cluster algorithms, leading to significant improvement when facing clusters located in different subspaces.

Reference

T. Ortner, P. Filzmoser, S. Brodinova, M. Zaharieva, C. Breiteneder: "Forward Projection for High-Dimensional Data"; Talk: International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus; 09-06-2016 - 09-10-2016.

BibTeX

@unpublished{TUW-252054,     author = {Ortner, Thomas and Filzmoser, Peter and Brodinova, Sarka and Zaharieva, Maia and Breiteneder, Christian},     title = {Forward Projection for High-Dimensional Data},     year = {2016},     keywords = {high-dimensional data, subspace projections, diagnostic plots},     note = {talk: International Conference COMPUTER DATA ANALYSIS {\&} MODELING, Minsk, Belarus; 2016-09-06 -- 2016-09-10} } Click into the text area and press Ctrl+A/Ctrl+C or ⌘+A/⌘+C to copy the BibTeX into your clipboard… or download the BibTeX.