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TIP
2010
182views more  TIP 2010»
9 years 6 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
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 ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
JMLR
2010
150views more  JMLR 2010»
9 years 6 months ago
Supervised Dimension Reduction Using Bayesian Mixture Modeling
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
Kai Mao, Feng Liang, Sayan Mukherjee
CIKM
2010
Springer
9 years 9 months ago
Fast dimension reduction for document classification based on imprecise spectrum analysis
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...
Hu Guan, Bin Xiao, Jingyu Zhou, Minyi Guo, Tao Yan...
SAC
2010
ACM
9 years 10 months ago
Optimal linear projections for enhancing desired data statistics
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...
Evgenia Rubinshtein, Anuj Srivastava
IJON
2010
152views more  IJON 2010»
9 years 10 months ago
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data
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...
Kerstin Bunte, Barbara Hammer, Axel Wismüller...
IDEAL
2010
Springer
9 years 10 months ago
Dimension Reduction for Regression with Bottleneck Neural Networks
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 ...
Elina Parviainen
FTML
2010
159views more  FTML 2010»
9 years 10 months ago
Dimension Reduction: A Guided Tour
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...
Christopher J. C. Burges
JCP
2007
149views more  JCP 2007»
9 years 11 months ago
Partitional Clustering Techniques for Multi-Spectral Image Segmentation
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....
Danielle Nuzillard, Cosmin Lazar
IJAIT
2007
108views more  IJAIT 2007»
9 years 11 months ago
Document Retrieval by Projection Based Frequency Distribution
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...
Isamu Shioya, Hirohito Oh'uchi, Takao Miura
NPL
2006
130views more  NPL 2006»
9 years 11 months ago
A Fast Feature-based Dimension Reduction Algorithm for Kernel Classifiers
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...
Senjian An, Wanquan Liu, Svetha Venkatesh, Ronny T...
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