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BMVC
2010
14 years 7 months ago
Iterative Hyperplane Merging: A Framework for Manifold Learning
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Harry Strange, Reyer Zwiggelaar
DEXA
2006
Springer
190views Database» more  DEXA 2006»
15 years 1 months ago
High-Dimensional Similarity Search Using Data-Sensitive Space Partitioning
Abstract. Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been ...
Sachin Kulkarni, Ratko Orlandic
PEWASUN
2006
ACM
15 years 3 months ago
Wave propagation using the photon path map
In wireless network planning, much effort is spent on the improvement of the network and transport layer – especially for Mobile Ad Hoc Networks. Although in principle realworl...
Arne Schmitz, Leif Kobbelt
ICDM
2005
IEEE
165views Data Mining» more  ICDM 2005»
15 years 3 months ago
Orthogonal Neighborhood Preserving Projections
— Orthogonal Neighborhood Preserving Projections (ONPP) is a linear dimensionality reduction technique which attempts to preserve both the intrinsic neighborhood geometry of the ...
Effrosini Kokiopoulou, Yousef Saad
ACMACE
2008
ACM
14 years 11 months ago
Dimensionality reduced HRTFs: a comparative study
Dimensionality reduction is a statistical tool commonly used to map high-dimensional data into lower a dimensionality. The transformed data is typically more suitable for regressi...
Bill Kapralos, Nathan Mekuz, Agnieszka Kopinska, S...