Add to ORKGWe present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean space from random samples. The method is based on the convergence rates of a certain U-statistic on the manifold. We solve at least partially the question of the choice of the scale of the data. Moreover the proposed method is easy to implement, can handle large data sets and performs very well even for small sample sizes. We compare the proposed method to two standard estimators on several artificial as well as real data sets
In the past two decades the estimation of the intrinsic dimensionality of a dataset has gained consi...
Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionali...
This thesis concerns the problem of dimensionality reduction through information geometric methods o...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in R d from rand...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean spa...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean spa...
34 pages, 4 figuresWe give explicit theoretical and heuristical bounds for how big does a data set s...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
In this paper we investigate the dimensional structure of probability distributions on Euclidean spa...
When dealing with datasets comprising high-dimensional points, it is usually advantageous to discove...
AbstractIn this paper we investigate the dimensional structure of probability distributions on Eucli...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
dissertationIntrinsic dimension estimation is a fundamental problem in manifold learning. In applica...
A realization fi(·) from a class F(·) can be represented as a point in a metric space and the locus ...
Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of ...
In the past two decades the estimation of the intrinsic dimensionality of a dataset has gained consi...
Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionali...
This thesis concerns the problem of dimensionality reduction through information geometric methods o...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in R d from rand...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean spa...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean spa...
34 pages, 4 figuresWe give explicit theoretical and heuristical bounds for how big does a data set s...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
In this paper we investigate the dimensional structure of probability distributions on Euclidean spa...
When dealing with datasets comprising high-dimensional points, it is usually advantageous to discove...
AbstractIn this paper we investigate the dimensional structure of probability distributions on Eucli...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
dissertationIntrinsic dimension estimation is a fundamental problem in manifold learning. In applica...
A realization fi(·) from a class F(·) can be represented as a point in a metric space and the locus ...
Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of ...
In the past two decades the estimation of the intrinsic dimensionality of a dataset has gained consi...
Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionali...
This thesis concerns the problem of dimensionality reduction through information geometric methods o...