Neighbor Embedding by Soft Kendall Correlation
Date
2013
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Correlation-based embedding of complex data relationships in a Euclidean space is studied. The proposed soft formulation of Kendall correlation allows for gradient-based optimization of scatter point neighborhood relationships for reconstructing original data neighbors. The approach is able to handle asymmetric data relations provided in the form of a general scoring matrix. Scale and shift invariance properties of correlation help circumventing typical embedding distortion artefacts in dimension reduction and data embedding scenarios.
Description
@inproceedings{:10.2312/PE.EuroVisShort.EuroVisShort2013.073-077,
booktitle = {EuroVis - Short Papers},
editor = {Mario Hlawitschka and Tino Weinkauf},
title = {{Neighbor Embedding by Soft Kendall Correlation}},
author = {Strickert, Marc and Hüllermeier, Eyke},
year = {2013},
publisher = {The Eurographics Association},
ISBN = {978-3-905673-99-9},
DOI = {/10.2312/PE.EuroVisShort.EuroVisShort2013.073-077}
}