KERNELS AND KERNEL METHODS FOR SPECTRAL DATA
First Claim
1. A method for analysis of data contained in a plurality of spectra generated from a plurality of samples comprising a plurality of different sample classes, the method comprising:
- aligning the plurality of spectra;
constructing a similarity measure for comparing pairs of samples;
training at least one support vector machine to discriminate between the plurality of different sample classes, wherein the at least one support vector machine comprises a kernel;
processing the plurality of spectra using the at least one support vector machine;
identifying at least one predictive feature within the plurality of spectra.
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Abstract
Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.
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Citations
11 Claims
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1. A method for analysis of data contained in a plurality of spectra generated from a plurality of samples comprising a plurality of different sample classes, the method comprising:
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aligning the plurality of spectra;
constructing a similarity measure for comparing pairs of samples;
training at least one support vector machine to discriminate between the plurality of different sample classes, wherein the at least one support vector machine comprises a kernel;
processing the plurality of spectra using the at least one support vector machine;
identifying at least one predictive feature within the plurality of spectra. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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Specification