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» Clustering Large Dynamic Datasets Using Exemplar Points
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SIGMOD
1996
ACM
151views Database» more  SIGMOD 1996»
13 years 9 months ago
BIRCH: An Efficient Data Clustering Method for Very Large Databases
Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters...
Tian Zhang, Raghu Ramakrishnan, Miron Livny
KDD
2006
ACM
201views Data Mining» more  KDD 2006»
14 years 5 months ago
Clustering based large margin classification: a scalable approach using SOCP formulation
This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture...
J. Saketha Nath, Chiranjib Bhattacharyya, M. Naras...

Publication
197views
12 years 1 months ago
Convex non-negative matrix factorization for massive datasets
Non-negative matrix factorization (NMF) has become a standard tool in data mining, information retrieval, and signal processing. It is used to factorize a non-negative data matrix ...
C. Thurau, K. Kersting, M. Wahabzada, and C. Bauck...
ICRA
2009
IEEE
163views Robotics» more  ICRA 2009»
13 years 12 months ago
On fast surface reconstruction methods for large and noisy point clouds
— In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface...
Zoltan Csaba Marton, Radu Bogdan Rusu, Michael Bee...
CVPR
2010
IEEE
14 years 1 months ago
Dynamical Binary Latent Variable Models for 3D Human Pose Tracking
We introduce a new class of probabilistic latent variable model called the Implicit Mixture of Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. K...
Graham Taylor, Leonid Sigal, David Fleet, Geoffrey...