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» A Privacy Preserving Framework for Gaussian Mixture Models
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ICCV
2009
IEEE
15 years 1 months ago
Adaptive Fragments-Based Tracking of Non-Rigid Objects Using Level Sets
We present an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint feature...
Prakash Chockalingam, Nalin Pradeep
ICASSP
2011
IEEE
13 years 10 days ago
Dirichlet Mixture Models of neural net posteriors for HMM-based speech recognition
In this paper, we present a novel technique for modeling the posterior probability estimates obtained from a neural network directly in the HMM framework using the Dirichlet Mixtu...
Balakrishnan Varadarajan, Garimella S. V. S. Sivar...
ESANN
2004
13 years 10 months ago
Flexible and Robust Bayesian Classification by Finite Mixture Models
Abstract. The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its princip...
Cédric Archambeau, Frédéric V...
GRC
2007
IEEE
13 years 8 months ago
Privacy Preserving Collaborative Filtering Using Data Obfuscation
Collaborative filtering (CF) systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. Th...
Rupa Parameswaran, Douglas M. Blough
ICMCS
2009
IEEE
104views Multimedia» more  ICMCS 2009»
13 years 6 months ago
A variational multi-view learning framework and its application to image segmentation
The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the g...
Zhenglong Li, Qingshan Liu, Hanqing Lu