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» Dimensionality Reduction with Adaptive Kernels
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SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
14 years 7 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
CVPR
2007
IEEE
14 years 9 months ago
Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...
Pablo Arias, Gregory Randall, Guillermo Sapiro
UM
2009
Springer
14 years 2 months ago
PerspectiveSpace: Opinion Modeling with Dimensionality Reduction
Abstract. Words mean different things to different people, and capturing these differences is often a subtle art. These differences are often “a matter of perspective,” and...
Jason B. Alonso, Catherine Havasi, Henry Lieberman
CVPR
2011
IEEE
13 years 2 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
TIP
2011
162views more  TIP 2011»
13 years 2 months ago
Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations
—This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formu...
Allan Aasbjerg Nielsen