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IJCV
1998
105views more  IJCV 1998»
14 years 11 months ago
Robust Algorithms for Object Localization
Object localization using sensed data features and corresponding model features is a fundamental problem in machine vision. We reformulate object localization as a least squares p...
Aaron S. Wallack, Dinesh Manocha
KDD
2010
ACM
242views Data Mining» more  KDD 2010»
15 years 1 months ago
A scalable two-stage approach for a class of dimensionality reduction techniques
Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formula...
Liang Sun, Betul Ceran, Jieping Ye
STOC
2001
ACM
111views Algorithms» more  STOC 2001»
16 years 3 days ago
Optimal outlier removal in high-dimensional
We study the problem of finding an outlier-free subset of a set of points (or a probability distribution) in n-dimensional Euclidean space. As in [BFKV 99], a point x is defined t...
John Dunagan, Santosh Vempala
COMGEO
2011
ACM
14 years 6 months ago
Covering points by disjoint boxes with outliers
For a set of n points in the plane, we consider the axis–aligned (p, k)-Box Covering problem: Find p axis-aligned, pairwise-disjoint boxes that together contain at least n − k...
Hee-Kap Ahn, Sang Won Bae, Erik D. Demaine, Martin...
CVPR
2005
IEEE
16 years 1 months ago
Robust L1 Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming
Matrix factorization has many applications in computer vision. Singular Value Decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing ...
Qifa Ke, Takeo Kanade