219 search results - page 11 / 44 » Learning the Dimensionality of Hidden Variables |
ECCV
2010
Springer
14 years 10 months ago
2010
Springer
Abstract. We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at appl...
75
Voted
COLT
2010
Springer
14 years 7 months ago
2010
Springer
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of obse...
KDD
2004
ACM
15 years 10 months ago
2004
ACM
Transformation of both the response variable and the predictors is commonly used in fitting regression models. However, these transformation methods do not always provide the maxi...
APPROX
2008
Springer
14 years 11 months ago
2008
Springer
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
71
Voted
IJCNN
2006
IEEE
15 years 3 months ago
2006
IEEE
- This paper presents a variable node-to-node-link neural network (VN2 NN) trained by real-coded genetic algorithm (RCGA). The VN2 NN exhibits a node-to-node relationship in the hi...