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» Learning the Structure of Linear Latent Variable Models
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NIPS
2004
13 years 10 months ago
Multiple Alignment of Continuous Time Series
Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained i...
Jennifer Listgarten, Radford M. Neal, Sam T. Rowei...
WEBI
2004
Springer
14 years 2 months ago
Mining Local Data Sources For Learning Global Cluster Models
— Distributed data mining has recently caught a lot of attention as there are many cases where pooling distributed data for mining is probibited, due to either huge data volume o...
Chak-Man Lam, Xiaofeng Zhang, William Kwok-Wai Che...
UAI
2008
13 years 10 months ago
Feature Selection via Block-Regularized Regression
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one...
Seyoung Kim, Eric P. Xing
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
14 years 9 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
IDA
2009
Springer
13 years 6 months ago
Estimating Hidden Influences in Metabolic and Gene Regulatory Networks
We address the applicability of blind source separation (BSS) methods for the estimation of hidden influences in biological dynamic systems such as metabolic or gene regulatory net...
Florian Blöchl, Fabian J. Theis