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» Dimensionality Reduction for Classification
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COMPGEOM
2007
ACM
15 years 7 months ago
Embeddings of surfaces, curves, and moving points in euclidean space
In this paper we show that dimensionality reduction (i.e., Johnson-Lindenstrauss lemma) preserves not only the distances between static points, but also between moving points, and...
Pankaj K. Agarwal, Sariel Har-Peled, Hai Yu
NIPS
2004
15 years 4 months ago
Multiple Relational Embedding
We describe a way of using multiple different types of similarity relationship to learn a low-dimensional embedding of a dataset. Our method chooses different, possibly overlappin...
Roland Memisevic, Geoffrey E. Hinton
IVC
2007
184views more  IVC 2007»
15 years 3 months ago
Image distance functions for manifold learning
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
Richard Souvenir, Robert Pless
95
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ICPR
2006
IEEE
16 years 4 months ago
An Empirical Model for Saturation and Capacity in Classifier Spaces
When assessing reported classification results based on selection of members from a database (e.g. a face database), one would like to know what is an achievable classification ra...
Robert B. Fisher
ICPR
2002
IEEE
16 years 4 months ago
Fast Projection Plane Classifier
In this paper a new approach for classification is presented, the "Fast Projection Plane Classfier", abbreviated here to FPPC. The main idea of FPPC is very simple: A cl...
Dirk Balthasar, Lutz Priese