Metric learning for nearest class mean classifiers
First Claim
1. A classification method comprising:
- with a processor, for a new sample to be classified, for each of a set of classes, computing a probability for each class based on a comparison measure between a multidimensional representation of the new sample and a respective multidimensional class representation, the comparison measure being computed in a space of lower dimensionality than the multidimensional representation of the new sample by embedding the multidimensional representation of the new sample and the multidimensional class representations with a projection that has been learned on labeled samples to optimize classification of the labeled samples based on the comparison measure, the comparison measure being based on an exponentially decreasing function of a distance between the embedded multidimensional representation of the sample and a respective one of the embedded multidimensional class representations, each multidimensional class representation being computed based on a set of multidimensional representations of labeled samples that are labeled with the respective class; and
assigning a class to the new sample based on the computed probabilities.
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Abstract
A classification system and method enable improvements to classification with nearest class mean classifiers by computing a comparison measure between a multidimensional representation of a new sample and a respective multidimensional class representation embedded into a space of lower dimensionality than that of the multidimensional representations. The embedding is performed with a projection that has been learned on labeled samples to optimize classification with respect to multidimensional class representations for classes which may be the same or different from those used subsequently for classification. Each multidimensional class representation is computed as a function of a set of multidimensional representations of labeled samples, each labeled with the respective class. A class is assigned to the new sample based on the computed comparison measures.
8 Citations
27 Claims
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1. A classification method comprising:
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with a processor, for a new sample to be classified, for each of a set of classes, computing a probability for each class based on a comparison measure between a multidimensional representation of the new sample and a respective multidimensional class representation, the comparison measure being computed in a space of lower dimensionality than the multidimensional representation of the new sample by embedding the multidimensional representation of the new sample and the multidimensional class representations with a projection that has been learned on labeled samples to optimize classification of the labeled samples based on the comparison measure, the comparison measure being based on an exponentially decreasing function of a distance between the embedded multidimensional representation of the sample and a respective one of the embedded multidimensional class representations, each multidimensional class representation being computed based on a set of multidimensional representations of labeled samples that are labeled with the respective class; and assigning a class to the new sample based on the computed probabilities. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A system comprising:
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memory which stores; a projection matrix for embedding multidimensional representations into an embedding space, the projection matrix having been learned from class-labeled samples to optimize a classification rate on the labeled samples with nearest class mean classifiers; and a nearest class mean classifier for each of a set of classes, each of the nearest class mean classifiers in the set being computed based on multidimensional representations of samples that are labeled with the respective class; instructions for; computing a comparison measure between a multidimensional representation of a new sample and each of the nearest class mean classifiers, the comparison measure being computed in the embedding space in which the multidimensional representation of the new sample and the nearest class mean classifiers are embedded with the projection matrix, the comparison measure being based on an exponentially decreasing function of a distance between the embedded multidimensional representation of the new sample and a respective one of the embedded multidimensional class representations, and outputting information based on the comparison measure; and a processor in communication with the memory which implements the instructions. - View Dependent Claims (24, 25)
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26. A method of generating a classification system, comprising:
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providing a multidimensional representation and a class label for each of a set of training samples, each of the class labels corresponding to a respective one of a set of classes; computing a nearest class mean classifier for each of the classes, based on the multidimensional representations of training samples labeled with that class; with a processor, learning a projection based on the multidimensional representations, class labels, and nearest class mean classifiers which embeds the multidimensional representations and nearest class mean classifiers into an embedding space that optimizes a classification of the training samples by the set of nearest class mean classifiers in the embedding space, the learning of the projection aims to minimize the negative log-likelihood of the class labels yiε
{1, . . . , C} of the training samples according to the objective function; - View Dependent Claims (27)
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Specification