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Graph-based Extreme Feature Selection for Multi-class Classification Tasks
We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task.
Multi-View Kernels for Low-Dimensional Modeling of Seismic Events
The problem of learning from seismic recordings has been studied for years.
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis
Non-linear dimensionality reduction techniques such as manifold learning algorithms have become a common way for processing and analyzing high-dimensional patterns that often have attached a target that corresponds to the value of an unknown function.
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