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» Structured metric learning for high dimensional problems
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IJCV
2006
206views more  IJCV 2006»
13 years 7 months ago
Random Sampling for Subspace Face Recognition
Subspacefacerecognitionoftensuffersfromtwoproblems:(1)thetrainingsamplesetissmallcompared with the high dimensional feature vector; (2) the performance is sensitive to the subspace...
Xiaogang Wang, Xiaoou Tang
ICASSP
2011
IEEE
12 years 11 months ago
Sensing-aware classification with high-dimensional data
In many applications decisions must be made about the state of an object based on indirect noisy observation of highdimensional data. An example is the determination of the presen...
Burkay Orten, Prakash Ishwar, W. Clem Karl, Venkat...
VISUALIZATION
1997
IEEE
13 years 11 months ago
ROAMing terrain: real-time optimally adapting meshes
Terrain visualization is a difficult problem for applications requiring accurate images of large datasets at high frame rates, such as flight simulation and ground-based aircraf...
Mark A. Duchaineau, Murray Wolinsky, David E. Sige...
COMPGEOM
2006
ACM
14 years 1 months ago
Embedding ultrametrics into low-dimensional spaces
We study the problem of minimum-distortion embedding of ultrametrics into the plane and higher dimensional spaces. Ultrametrics are a natural class of metrics that frequently occu...
Mihai Badoiu, Julia Chuzhoy, Piotr Indyk, Anastasi...
ICTA
2009
IEEE
14 years 2 months ago
Feasibility Analysis for SemiAutomatic Conversion of Text to Improve Readability
1 WCAG 2.0 (W3C Recommendation 11 Dec. 2008) recommendation includes a specific guideline regarding the readability of text contents. This paper presents an approach to adapt text ...
Susana Bautista, Pablo Gervás, R. Ignacio M...