TIP
2010
13 years 5 months ago
2010
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
JMLR
2010
13 years 5 months ago
2010
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
CIKM
2010
Springer
13 years 8 months ago
2010
Springer
This paper proposes an algorithm called Imprecise Spectrum Analysis (ISA) to carry out fast dimension reduction for document classification. ISA is designed based on the one-sided...
SAC
2010
ACM
13 years 9 months ago
2010
ACM
Problems involving high-dimensional data, such as pattern recognition, image analysis, and gene clustering, often require a preliminary step of dimension reduction before or durin...
IJON
2010
13 years 9 months ago
2010
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensiona...
IDEAL
2010
Springer
13 years 9 months ago
2010
Springer
Dimension reduction for regression (DRR) deals with the problem of finding for high-dimensional data such low-dimensional representations, which preserve the ability to predict a ...
FTML
2010
13 years 9 months ago
2010
We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the da...
JCP
2007
13 years 10 months ago
2007
Abstract— Analyzing unknown data sets such as multispectral images often requires unsupervised techniques. Data clustering is a well known and widely used approach in such cases....
IJAIT
2007
13 years 10 months ago
2007
In document retrieval task, random projection (RP) is a useful technique of dimension reduction. It can be obtained very quickly yet the recalculation is not necessary to any chang...
NPL
2006
13 years 10 months ago
2006
This paper presents a novel dimension reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-c...