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NN
2010
Springer
183views Neural Networks» more  NN 2010»
13 years 5 months ago
Dimensionality reduction for density ratio estimation in high-dimensional spaces
The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various d...
Masashi Sugiyama, Motoaki Kawanabe, Pui Ling Chui
ICCV
2007
IEEE
14 years 9 months ago
Discriminant Embedding for Local Image Descriptors
Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and visual recognition. However, such descriptors are generally par...
Gang Hua, Matthew Brown, Simon A. J. Winder
CAIP
1997
Springer
125views Image Analysis» more  CAIP 1997»
13 years 11 months ago
An Algorithm for Intrinsic Dimensionality Estimation
Abstract. In this paper a new method for analyzing the intrinsic dimensionality (ID) of low dimensional manifolds in high dimensional feature spaces is presented. The basic idea is...
Jörg Bruske, Gerald Sommer
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
ICML
2007
IEEE
14 years 8 months ago
Map building without localization by dimensionality reduction techniques
This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is...
Takehisa Yairi