Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A dependence maximization view of clustering
Proceedings of the 24th international conference on Machine learning
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
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The main motivation of this paper is to propose a method to extract the structure information from the output data and find the input data manifold that best represents that output structure. A graph similarity viewpoint is used to build up a clustering algorithm that tries to find out different linear models in a regression framework. The main novelty of the algorithm is related with using the structured information of the output data, to find out several input models that best represent that structure. This novelty is base on the intuition that similar structures in the output must share a common model. Finally, the proposed method is applied to a real remote sensing retrieval problem where we want to recover the physical parameters from a spectrum of energy.