A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
OP-ELM: Theory, Experiments and a Toolbox
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
OP-KNN: method and applications
Advances in Artificial Neural Systems
Two-stage extreme learning machine for regression
Neurocomputing
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Extreme learning machine: a robust modeling technique? yes!
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Long-term time series prediction using OP-ELM
Neural Networks
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Sparse regression is the problem of selecting a parsimonious subset of all available regressors for an efficient prediction of a target variable. We consider a general setting in which both the target and regressors may be multivariate. The regressors are selected by a forward selection procedure that extends the Least Angle Regression algorithm. Instead of the common practice of estimating each target variable individually, our proposed method chooses sequentially those regressors that allow, on average, the best predictions of all the target variables. We illustrate the procedure by an experiment with artificial data. The method is also applied to the task of selecting relevant pixels from images in multidimensional scaling of handwritten digits.