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» On Approximating the Radii of Point Sets in High Dimensions
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NIPS
2008
13 years 9 months ago
Diffeomorphic Dimensionality Reduction
This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high a...
Christian Walder, Bernhard Schölkopf
MICRO
2006
IEEE
127views Hardware» more  MICRO 2006»
14 years 1 months ago
A Predictive Performance Model for Superscalar Processors
Designing and optimizing high performance microprocessors is an increasingly difficult task due to the size and complexity of the processor design space, high cost of detailed si...
P. J. Joseph, Kapil Vaswani, Matthew J. Thazhuthav...
SIGMOD
2009
ACM
120views Database» more  SIGMOD 2009»
14 years 7 months ago
Kernel-based skyline cardinality estimation
The skyline of a d-dimensional dataset consists of all points not dominated by others. The incorporation of the skyline operator into practical database systems necessitates an ef...
Zhenjie Zhang, Yin Yang, Ruichu Cai, Dimitris Papa...
NIPS
2007
13 years 9 months ago
Random Projections for Manifold Learning
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
Chinmay Hegde, Michael B. Wakin, Richard G. Barani...
CVPR
2008
IEEE
14 years 9 months ago
Dimensionality reduction using covariance operator inverse regression
We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
Minyoung Kim, Vladimir Pavlovic