Soft rank neighbor embeddings

Marc Strickert; Kerstin Bunte

Soft rank neighbor embeddings

Marc Strickert
, Kerstin Bunte

University of Marburg
Bielefeld University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

Abstract

Correlation-based multidimensional scaling is proposed for reconstructing pairwise dissimilarity or score relationships in a Euclidean space. Pearson correlation between pairs of objects in source and target space can be directly maximized by gradient methods, while gradient optimization of Spearman rank correlation profits from a numerically soft formulation introduced in this work. Scale and shift invariance properties of correlation help circumventing typical distance concentration problems.

Original language	English
Title of host publication	ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages	77-82
Number of pages	6
Publication status	Published - 2013
MoE publication type	A4 Conference publication
Event	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium Duration: 24 Apr 2013 → 26 Apr 2013 Conference number: 21

Conference

Conference	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated title	ESANN
Country/Territory	Belgium
City	Bruges
Period	24/04/2013 → 26/04/2013

Cite this

@inproceedings{b37165bf27144932a7352e101e7994e6,

title = "Soft rank neighbor embeddings",

abstract = "Correlation-based multidimensional scaling is proposed for reconstructing pairwise dissimilarity or score relationships in a Euclidean space. Pearson correlation between pairs of objects in source and target space can be directly maximized by gradient methods, while gradient optimization of Spearman rank correlation profits from a numerically soft formulation introduced in this work. Scale and shift invariance properties of correlation help circumventing typical distance concentration problems.",

author = "Marc Strickert and Kerstin Bunte",

year = "2013",

language = "English",

isbn = "9782874190810",

pages = "77--82",

booktitle = "ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

note = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN ; Conference date: 24-04-2013 Through 26-04-2013",

}

TY - GEN

T1 - Soft rank neighbor embeddings

AU - Strickert, Marc

AU - Bunte, Kerstin

N1 - Conference code: 21

PY - 2013

Y1 - 2013

N2 - Correlation-based multidimensional scaling is proposed for reconstructing pairwise dissimilarity or score relationships in a Euclidean space. Pearson correlation between pairs of objects in source and target space can be directly maximized by gradient methods, while gradient optimization of Spearman rank correlation profits from a numerically soft formulation introduced in this work. Scale and shift invariance properties of correlation help circumventing typical distance concentration problems.

AB - Correlation-based multidimensional scaling is proposed for reconstructing pairwise dissimilarity or score relationships in a Euclidean space. Pearson correlation between pairs of objects in source and target space can be directly maximized by gradient methods, while gradient optimization of Spearman rank correlation profits from a numerically soft formulation introduced in this work. Scale and shift invariance properties of correlation help circumventing typical distance concentration problems.

UR - https://www.scopus.com/pages/publications/84887068040

M3 - Conference article in proceedings

AN - SCOPUS:84887068040

SN - 9782874190810

SP - 77

EP - 82

BT - ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

T2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 24 April 2013 through 26 April 2013

ER -

Soft rank neighbor embeddings

Abstract

Conference

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