Generation of Representations for Supervised Learning - a Velocity Estimation Example
نویسندگان
چکیده
A two-step learning method for velocity estimation is presented. First, an efficient representation of velocity is found using a learning technique based on canonical correlation analysis. This results in a spherical representation. Then, given this new representation, the mapping from input data to velocity estimates are learned by minimizing the mean square error between the output and the desired output on a training set. The non-linear mapping on the ’velocitysphere’ representation improves the performance of the linear method in the supervised learning step.
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