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» Hierarchical Gaussian process latent variable models
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CVIU
2011
14 years 3 months ago
Single and sparse view 3D reconstruction by learning shape priors
In this paper, we aim to reconstruct free-form 3D models from only one or few silhouettes by learning the prior knowledge of a specific class of objects. Instead of heuristically...
Yu Chen, Roberto Cipolla
ICDM
2010
IEEE
166views Data Mining» more  ICDM 2010»
14 years 9 months ago
Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection
In this paper, we study probabilistic modeling of heterogeneously attributed multi-dimensional arrays. The model can manage the heterogeneity by employing an individual exponential...
Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibat...
ICDM
2005
IEEE
165views Data Mining» more  ICDM 2005»
15 years 5 months ago
A Bernoulli Relational Model for Nonlinear Embedding
The notion of relations is extremely important in mathematics. In this paper, we use relations to describe the embedding problem and propose a novel stochastic relational model fo...
Gang Wang, Hui Zhang, Zhihua Zhang, Frederick H. L...
GRC
2010
IEEE
15 years 24 days ago
Learning Multiple Latent Variables with Self-Organizing Maps
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
Lili Zhang, Erzsébet Merényi
DAC
2007
ACM
16 years 21 days ago
Beyond Low-Order Statistical Response Surfaces: Latent Variable Regression for Efficient, Highly Nonlinear Fitting
The number and magnitude of process variation sources are increasing as we scale further into the nano regime. Today's most successful response surface methods limit us to lo...
Amith Singhee, Rob A. Rutenbar