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» Automated Extraction of Problem Structure
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DAGSTUHL
2009
13 years 10 months ago
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Abstract. In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call su...
Erzsébet Merényi, Kadim Tasdemir, Li...
KDD
2007
ACM
201views Data Mining» more  KDD 2007»
14 years 9 months ago
Structural and temporal analysis of the blogosphere through community factorization
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel tech...
Yun Chi, Shenghuo Zhu, Xiaodan Song, Jun'ichi Tate...
ICCV
2011
IEEE
12 years 9 months ago
Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...
Guangcan Liu, Shuicheng Yan
SIGMOD
2011
ACM
242views Database» more  SIGMOD 2011»
12 years 12 months ago
The SystemT IDE: an integrated development environment for information extraction rules
Information Extraction (IE) — the problem of extracting structured information from unstructured text — has become the key enabler for many enterprise applications such as sem...
Laura Chiticariu, Vivian Chu, Sajib Dasgupta, Thil...
CORR
2010
Springer
152views Education» more  CORR 2010»
13 years 9 months ago
Microlocal Analysis of the Geometric Separation Problem
Image data are often composed of two or more geometrically distinct constituents; in galaxy catalogs, for instance, one sees a mixture of pointlike structures (galaxy supercluster...
David L. Donoho, Gitta Kutyniok