Low Bias Local Intrinsic Dimension Estimation from Expected Simplex Skewness

Johnsson, Kerstin (författare): Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH

Soneson, Charlotte (författare)

Fontes, Magnus (författare): Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH

(creator_code:org_t)

2015
2015
Engelska.
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 37:1, s. 196-202

Relaterad länk:: http://dx.doi.org/10... (free); visa fler...; https://lup.lub.lu.s...; https://doi.org/10.1...; visa färre...

Abstract Ämnesord

Stäng

In exploratory high-dimensional data analysis, local intrinsic dimension estimation can sometimes be used in order to discriminate between data sets sampled from different low-dimensional structures. Global intrinsic dimension estimators can in many cases be adapted to local estimation, but this leads to problems with high negative bias or high variance. We introduce a method that exploits the curse/blessing of dimensionality and produces local intrinsic dimension estimators that have very low bias, even in cases where the intrinsic dimension is higher than the number of data points, in combination with relatively low variance. We show that our estimators have a very good ability to classify local data sets by their dimension compared to other local intrinsic dimension estimators; furthermore we provide examples showing the usefulness of local intrinsic dimension estimation in general and our method in particular for stratification of real data sets.

Av författaren/redakt...: Johnsson, Kersti ...; Soneson, Charlot ...; Fontes, Magnus

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