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» Missing Data Estimation Using Polynomial Kernels
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ICA
2007
Springer
15 years 6 months ago
On Separation of Signal Sources Using Kernel Estimates of Probability Densities
The discussion in this paper revolves around the notion of separation problems. The latter can be thought of as a unifying concept which includes a variety of important problems in...
Oleg V. Michailovich, Douglas Wiens
SDM
2011
SIAM
284views Data Mining» more  SDM 2011»
14 years 3 months ago
The Network Completion Problem: Inferring Missing Nodes and Edges in Networks
While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomp...
Myunghwan Kim 0002, Jure Leskovec
80
Voted
ICML
2005
IEEE
16 years 1 months ago
Incomplete-data classification using logistic regression
A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Single or multiple imputation for the missing...
David Williams, Xuejun Liao, Ya Xue, Lawrence Cari...
117
Voted
BMCBI
2010
150views more  BMCBI 2010»
14 years 10 months ago
Kernel based methods for accelerated failure time model with ultra-high dimensional data
Background: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lp penalty have been extensively studied in survi...
Zhenqiu Liu, Dechang Chen, Ming Tan, Feng Jiang, R...
EGITALY
2006
15 years 1 months ago
3D Data Segmentation Using a Non-Parametric Density Estimation Approach
In this paper, a new segmentation approach for sets of 3D unorganized points is proposed. The method is based on a clustering procedure that separates the modes of a non-parametri...
Umberto Castellani, Marco Cristani, Vittorio Murin...