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;
generating a population of chromosomes having encoded low-dimensionality coordinates for each of the kernels;
subjecting said population of chromosomes to evolutionary computation to generate new chromosomes and corresponding low-dimensionality coordinates for the kernels based on a fitness function until a threshold fitness level or predetermined number of iterations is reached, wherein the new chromosomes are used 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.
1 Assignment
0 Petitions
Accused Products
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.
-
Citations
25 Claims
-
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; generating a population of chromosomes having encoded low-dimensionality coordinates for each of the kernels; subjecting said population of chromosomes to evolutionary computation to generate new chromosomes and corresponding low-dimensionality coordinates for the kernels based on a fitness function until a threshold fitness level or predetermined number of iterations is reached, wherein the new chromosomes are used 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, 13, 14)
-
-
15. A visualization method that comprises:
-
obtaining a data set having a dimensionality that is to be reduced; generating a population of chromosomes having encoded low-dimensionality coordinates for data set members; subjecting said population of chromosomes to evolutionary computation to generate new chromosomes and corresponding low-dimensionality coordinates for data set members based on a fitness function until a threshold fitness level or predetermined number of iterations is reached, wherein the new chromosomes are used 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 (16, 17, 18, 19, 20)
-
-
21. A system employing dimensionality reduction, the system comprising:
-
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; generate a population of chromosomes having encoded low-dimensionality coordinates for data set members; subject said population of chromosomes to evolutionary computation to generate new chromosomes and corresponding low-dimensionality coordinates for data set members based on a fitness function until a threshold fitness level or predetermines number of iterations is reached; apply a particle swarm optimization to the new chromosomes to select a dimensionality reduction mapping; determine a low-dimensionality representation of the data set using the selected dimensionality reduction mapping; and output results to a user based on the low-dimensionality representation. - View Dependent Claims (22, 23, 24, 25)
-
Specification