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» Forecasting high-dimensional data
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ICML
2003
IEEE
15 years 10 months ago
On Kernel Methods for Relational Learning
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Chad M. Cumby, Dan Roth
CVPR
2010
IEEE
15 years 5 months ago
SPEC Hashing: Similarity Preserving algorithm for Entropy-based Coding
Searching approximate nearest neighbors in large scale high dimensional data set has been a challenging problem. This paper presents a novel and fast algorithm for learning binary...
Ruei-Sung Lin, David Ross, Jay Yagnik
IROS
2009
IEEE
200views Robotics» more  IROS 2009»
15 years 4 months ago
Fast geometric point labeling using conditional random fields
— In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor – the Fast Point Feature Histogram...
Radu Bogdan Rusu, Andreas Holzbach, Nico Blodow, M...
PSIVT
2009
Springer
400views Multimedia» more  PSIVT 2009»
15 years 4 months ago
Local Image Descriptors Using Supervised Kernel ICA
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Masaki Yamazaki, Sidney Fels
ICASSP
2008
IEEE
15 years 4 months ago
Mutual features for robust identification and verification
Noisy or distorted video/audio training sets represent constant challenges in automated identification and verification tasks. We propose the method of Mutual Interdependence An...
Heiko Claussen, Justinian Rosca, Robert I. Damper