Add to ORKGWe present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data.
Conference PaperRandom projections have recently found a surprising niche in signal processing. The ...
Random projection is a technique of mapping a number of points in a high-dimensional space into a lo...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...
The classic k-d tree data structure continues to be widely used in spite of its vulnerability to the...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
Manifold learning is a widely used statistical tool which reduces the dimensionality of a data set w...
A lot of manifold learning algorithms have been developed, which are used to learn a low dimensional...
Learning in high-dim. space is hard and expensive. Good news: intrinsic dimensionality is often low....
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. On...
The thesis focuses on solving problems that are related to the behavior of random variables in high...
We are increasingly confronted with very high dimensional data from speech,images, genomes, and othe...
Random projection is a simple geometric technique for reducing the dimensionality of a set of points...
Journal PaperMany types of data and information can be described by concise models that suggest each...
Manifold learning has shown powerful information processing capability for high-dimensional data. In...
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention ...
Conference PaperRandom projections have recently found a surprising niche in signal processing. The ...
Random projection is a technique of mapping a number of points in a high-dimensional space into a lo...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...
The classic k-d tree data structure continues to be widely used in spite of its vulnerability to the...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
Manifold learning is a widely used statistical tool which reduces the dimensionality of a data set w...
A lot of manifold learning algorithms have been developed, which are used to learn a low dimensional...
Learning in high-dim. space is hard and expensive. Good news: intrinsic dimensionality is often low....
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. On...
The thesis focuses on solving problems that are related to the behavior of random variables in high...
We are increasingly confronted with very high dimensional data from speech,images, genomes, and othe...
Random projection is a simple geometric technique for reducing the dimensionality of a set of points...
Journal PaperMany types of data and information can be described by concise models that suggest each...
Manifold learning has shown powerful information processing capability for high-dimensional data. In...
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention ...
Conference PaperRandom projections have recently found a surprising niche in signal processing. The ...
Random projection is a technique of mapping a number of points in a high-dimensional space into a lo...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...