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Talks and Poster Presentations (without Proceedings-Entry):

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; 2016-09-06 - 2016-09-10.



English 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.

German 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.

Keywords:
high-dimensional data, subspace projections, diagnostic plots


Related Projects:
Project Head Maia Zaharieva:
Unusual sequences detection in very large video collections


Created from the Publication Database of the Vienna University of Technology.