Data driven frequency mapping for kernels used in support vector machines
AssigneeMitsubishi Electric Research Laboratories, Inc.
InventorsFatih Porikli, Huseyin Ozkan
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Abstract
Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data.
Claims16
A method for selecting frequency features to be used for binary classification of data using a linear classifier, comprising the steps of:
- determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data, wherein a magnitude of each hypothesis vector is a spatial combing frequency;
- constructing a mapping function for each hypothesis;
- applying the mapping functions to the training data to generate frequency features;
- selecting iteratively a subset of the frequency features;
- and
- training the linear classifier using the subset of frequency features and labels of the training data, wherein the steps are performed in a processor.
A system for selecting frequency features to be used for binary classification of data using a linear classifier, comprising a processor for:
- determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data;
- constructing a mapping function for each hypothesis, wherein each hypothesis is a vector, and a magnitude of each hypothesis vector is a spatial combing frequency;
- applying the mapping functions to the training data to generate frequency features;
- selecting iteratively a subset of the frequency features;
- and
- training the linear classifier using the subset of frequency features and labels of the training data.
Dependencies are inferred from the claim text.