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» Adaptive metric dimensionality reduction
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DAGM
2006
Springer
13 years 11 months ago
Parameterless Isomap with Adaptive Neighborhood Selection
Abstract. Isomap is a highly popular manifold learning and dimensionality reduction technique that effectively performs multidimensional scaling on estimates of geodesic distances....
Nathan Mekuz, John K. Tsotsos
IJCNLP
2005
Springer
14 years 25 days ago
An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity
Abstract. This paper presents an empirical study on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three discriminative training mo...
Wei Yuan, Jianfeng Gao, Hisami Suzuki
CVPR
2000
IEEE
13 years 11 months ago
Adaptive Metric nearest Neighbor Classification
Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse o...
Carlotta Domeniconi, Dimitrios Gunopulos, Jing Pen...
FOCS
2003
IEEE
14 years 19 days ago
Bounded Geometries, Fractals, and Low-Distortion Embeddings
The doubling constant of a metric space (X, d) is the smallest value λ such that every ball in X can be covered by λ balls of half the radius. The doubling dimension of X is the...
Anupam Gupta, Robert Krauthgamer, James R. Lee
CIKM
2008
Springer
13 years 9 months ago
On low dimensional random projections and similarity search
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. However, many si...
Yu-En Lu, Pietro Liò, Steven Hand