Sciweavers

Share
140 search results - page 1 / 28
» Dimensionality Reduction with Adaptive Kernels
Sort
View
ICPR
2006
IEEE
13 years 2 months ago
Dimensionality Reduction with Adaptive Kernels
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...
Shuicheng Yan, Xiaoou Tang
IJCAI
2007
12 years 3 months ago
A Subspace Kernel for Nonlinear Feature Extraction
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-processing step in pattern classiļ¬cation and data mining tasks. Given a positive...
Mingrui Wu, Jason D. R. Farquhar
PKDD
2004
Springer
116views Data Mining» more  PKDD 2004»
12 years 7 months ago
Random Matrices in Data Analysis
We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certai...
Dimitris Achlioptas
ICML
2004
IEEE
12 years 7 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
ICIP
2005
IEEE
13 years 3 months ago
Nonlinear dimensionality reduction for classification using kernel weighted subspace method
We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weigh...
Guang Dai, Dit-Yan Yeung
books