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» Some Solutions to the Missing Feature Problem in Vision
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JMLR
2010
144views more  JMLR 2010»
12 years 11 months ago
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
Alexander Ilin, Tapani Raiko
ICIP
2008
IEEE
13 years 11 months ago
Globally optimal solution to exploit rigidity when recovering structure from motion under occlusion
Widely used SVD-based matrix factorization approaches to the recovery of 3D rigid structure from motion (SFM), require a set of feature points to be visible in a set of images. Wh...
Pedro M. Q. Aguiar, João M. F. Xavier, Mark...
ICCV
2001
IEEE
14 years 6 months ago
Classifying and Solving Minimal Structure and Motion Problems with Missing Data
In this paper we investigate the structure and motion problem for calibrated one-dimensional projections of a two-dimensional environment. In a previous paper the structure and mo...
Kalle Åström, Magnus Oskarsson, Niels C...
AUSAI
1998
Springer
13 years 8 months ago
The Problem of Missing Values in Decision Tree Grafting
Decision tree grafting adds nodes to inferred decision trees. Previous research has demonstrated that appropriate grafting techniques can improve predictive accuracy across a wide ...
Geoffrey I. Webb
CP
2008
Springer
13 years 6 months ago
Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies
Fuzzy constraints are a popular approach to handle preferences and over-constrained problems in scenarios where one needs to be cautious, such as in medical or space applications. ...
Mirco Gelain, Maria Silvia Pini, Francesca Rossi, ...