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» Structural Modelling with Sparse Kernels
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ICML
2006
IEEE
16 years 3 months ago
Nonstationary kernel combination
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
222
Voted
SDM
2011
SIAM
370views Data Mining» more  SDM 2011»
14 years 5 months ago
Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....
TIP
2010
189views more  TIP 2010»
15 years 25 days ago
Image Inpainting by Patch Propagation Using Patch Sparsity
Abstract—This paper introduces a novel examplar-based inpainting algorithm through investigating the sparsity of natural image patches. Two novel concepts of sparsity at the patc...
Zongben Xu, Jian Sun
WWW
2008
ACM
16 years 3 months ago
Computable social patterns from sparse sensor data
We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social foo...
Dinh Q. Phung, Brett Adams, Svetha Venkatesh
131
Voted
RECOMB
2005
Springer
16 years 2 months ago
Learning Interpretable SVMs for Biological Sequence Classification
Background: Support Vector Machines (SVMs) ? using a variety of string kernels ? have been successfully applied to biological sequence classification problems. While SVMs achieve ...
Christin Schäfer, Gunnar Rätsch, Sö...