Systems and Methods Employing Cooperative Optimization-Based Dimensionality Reduction
First Claim
1. A visualization method that comprises:
- obtaining a data set having a dimensionality that is to be reduced;
identifying kernels that represent clusters within the data set;
representing low-dimensionality coordinates of each kernel as a corresponding gene on a chromosome in a population of such chromosomes;
subjecting said population to evolutionary computation to select a dimensionality reduction mapping; and
displaying the kernels at locations based on their low-dimensionality coordinates as determined from the selected dimensionality reduction mapping.
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Abstract
Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.
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Citations
34 Claims
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1. A visualization method that comprises:
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obtaining a data set having a dimensionality that is to be reduced; identifying kernels that represent clusters within the data set; representing low-dimensionality coordinates of each kernel as a corresponding gene on a chromosome in a population of such chromosomes; subjecting said population to evolutionary computation to select a dimensionality reduction mapping; and displaying the kernels at locations based on their low-dimensionality coordinates as determined from the selected dimensionality reduction mapping. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A visualization method that comprises:
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obtaining a data set having a dimensionality that is to be reduced; representing low-dimensionality coordinates of data set members as corresponding genes in a population of chromosomes; subjecting said population to evolutionary computation to select a dimensionality reduction mapping; and displaying the data set members at locations based on their low-dimensionality coordinates as determined from the selected dimensionality reduction mapping. - View Dependent Claims (14, 15, 16, 17, 18)
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19. A substance fingerprinting method that comprises:
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performing a compositional analysis on a sample of a substance; applying a dimensionality reduction transform to results of the compositional analysis to obtain a low-dimensionality representation; using the low-dimensionality representation to match the sample with one or more closely-related substances in a data set; and identifying one or more characteristics of the sample based on properties of the closely-related substances. - View Dependent Claims (20, 21, 22, 23, 24, 25)
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26. A well-telemetry method that comprises:
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applying evolutionary computation to logging data to obtain a low-dimensionality encoding solution; training a neural network ensemble with the solution; configuring a downhole processor to apply the neural network ensemble to logging data to obtain reduced-dimension telemetry data for transmission uphole. - View Dependent Claims (27, 28, 29)
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30. A system employing dimensionality reduction, the system comprising:
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a memory having software; an output device; and a processor coupled to the memory to execute the software, wherein the software configures the processor to; obtain a high-dimensionality data set; determine a low-dimensionality representation of the data set using evolutionary computation with particle swarm optimization; and output results to a user based on the low-dimensionality representation. - View Dependent Claims (31, 32, 33, 34)
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Specification