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Elmar Diederichs

dblp:90/4301 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorTheory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
dimensionality reduction		 0.112010
Sparse non-Gaussian component analysis · IEEE Trans. Inf. Theory 2010
Data mining
high-dimensional data analysis		 0.012010
Sparse non-Gaussian component analysis · IEEE Trans. Inf. Theory 2010

Methods — techniques the papers use, named apart from their topics

convex projection · 0.1PCA-based subspace identification · 0.1
YearPublicationVenuePosition
2013 Sparse non Gaussian component analysis by semidefinite programming
Elmar Diederichs, Anatoli B. Juditsky, Arkadi Nemirovski, Vladimir G. Spokoiny
 Mach. Learn.1
2010 Sparse non-Gaussian component analysis
abstract
Non-Gaussian component analysis (NGCA) introduced in offered a method for high-dimensional data analysis allowing for identifying a low-dimensional non-Gaussian component of the whole distribution in an iterative and structure adaptive way. An important step of the NGCA procedure is identification of the non-Gaussian subspace using principle component analysis (PCA) method. This article proposes a new approach to NGCA called sparse NGCA which replaces the PCA-based procedure with a new the algorithm we refer to as convex projection.
Elmar Diederichs, Anatoli B. Juditsky, Vladimir G. Spokoiny, Christof Schütte
 IEEE Trans. Inf. Theory1