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» Local and Global Approximations for Incomplete Data
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99
Voted
ICML
2010
IEEE
15 years 21 days ago
Label Ranking Methods based on the Plackett-Luce Model
This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the ...
Weiwei Cheng, Krzysztof Dembczynski, Eyke Hül...
ICDM
2007
IEEE
169views Data Mining» more  ICDM 2007»
15 years 3 months ago
Efficient Discovery of Frequent Approximate Sequential Patterns
We propose an efficient algorithm for mining frequent approximate sequential patterns under the Hamming distance model. Our algorithm gains its efficiency by adopting a "brea...
Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu
JMLR
2010
367views more  JMLR 2010»
14 years 6 months ago
Locally Linear Denoising on Image Manifolds
We study the problem of image denoising where images are assumed to be samples from low dimensional (sub)manifolds. We propose the algorithm of locally linear denoising. The algor...
Dian Gong, Fei Sha, Gérard G. Medioni
93
Voted
ICDM
2008
IEEE
96views Data Mining» more  ICDM 2008»
15 years 6 months ago
Filling in the Blanks - Krimp Minimisation for Missing Data
Many data sets are incomplete. For correct analysis of such data, one can either use algorithms that are designed to handle missing data or use imputation. Imputation has the bene...
Jilles Vreeken, Arno Siebes
82
Voted
CSDA
2006
82views more  CSDA 2006»
14 years 11 months ago
Nearest neighbours in least-squares data imputation algorithms with different missing patterns
Methods for imputation of missing data in the so-called least-squares approximation approach, a non-parametric computationally efficient multidimensional technique, are experiment...
Ito Wasito, Boris Mirkin