Energy minimization for classification, pattern recognition, sensor fusion, data compression, network reconstruction and signal processing

• US 7,702,155 B2
• Filed: 04/01/2005
• Issued: 04/20/2010
• Est. Priority Date: 12/29/1997
• Status: Expired due to Fees

• Current Assignee: Abel Wolman, Jeff B. Glickman
• Original Assignee: Abel Wolman, Jeff B. Glickman
• Inventors: Wolman, Abel, Glickman, Jeff B.
• Primary Examiner(s): Desire, Gregory M
• Application Number:
  US11/097,783
• Publication Number:
  US 20050175244A1
• Time in Patent Office: 1,845 Days
• Field of Search: 345/440, 382/224, 382/298, 382/300, 382/158, 382/159, 382/170, 382/181, 382/280, 382/281, 706/16, 706/22, 706/25, 706/20
• US Class Current: 382/181
• CPC Class Codes: G06F 18/2137 based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation

Abstract

A data analyzer/classifier comprises using a preprocessing step, and energy minimization step, and a postprocessing step to analyze/classify data.

Claims

1. A method for determining dimensionality of a network, the dimensionality corresponding to a number of degrees of freedom in the network, the method comprising the steps of:

using a computer system, sampling data from one or more nodes of the network;

using the computer system, mapping the data into one or more matrices;

using the computer system, applying individual differences multidimensional scaling to the one or more matrices to produce a stress/energy; and

using the computer system, processing the stress/energy to determine the dimensionality of the network.

2. The method for determining dimensionality of a network of claim 1 wherein the step of mapping the data into one or more matrices comprises a processing step selected from the group consisting of forming one or more symmetric matrices, forming one or more hollow symmetric matrices, entry-wise substituting the received data to populate one or more symmetric matrices, forming one or more proximity matrices, forming one or more distance matrices, and forming one or more Euclidean distance matrices.

3. The method for determining dimensionality of a network of claim 1 wherein applying individual differences multidimensional scaling comprises applying nonmetric individual differences multidimensional scaling.

4. A method for determining dimensionality of a network, the dimensionality corresponding to a number of degrees of freedom in the network, the method comprising the steps of:

using a computer system, sampling data from one or more nodes of the network;

using the computer system, mapping the data into one or more matrices;

using the computer system, applying individual differences multidimensional scaling to the one or more matrices to produce a rate of change of stress/energy output and

using the computer system, processing the rate of change of stress/energy output to determine the dimensionality of the network.

5. The method for determining dimensionality of a network of claim 4 wherein the step of mapping the data into one or more matrices comprises a processing step selected from the group consisting of forming one or more symmetric matrices, forming one or more hollow symmetric matrices, entry-wise substituting the received data to populate one or more symmetric matrices, forming one or more proximity matrices, forming one or more distance matrices, and forming one or more Euclidean distance matrices.

6. The method for determining dimensionality of a network of claim 4 wherein the step of applying individual differences multidimensional scaling comprises applying nonmetric individual differences multidimensional scaling.

7. A method for determining dimensionality of a dynamical system from partial data, the dimensionality corresponding to a number of degrees of freedom in the dynamical system, the method comprising the steps of:

using a computer system, sampling data from the dynamical system;

using the computer system, mapping the data into one or more matrices;

using the computer system, applying individual differences multidimensional scaling to the one or more matrices to produce a stress/energy;

using the computer system, processing the stress/energy to determine dimensionality of the dynamical system.

8. The method determining dimensionality of a dynamical system from partial data of claim 7 wherein the step of mapping the data into one or more matrices comprises a processing step selected from the group consisting of forming one or more symmetric matrices, forming one or more hollow symmetric matrices, entry-wise substituting the received data to populate one or more symmetric matrices, forming one or more proximity matrices, forming one or more distance matrices, and forming one or more Euclidean distance matrices.

9. The method for determining dimensionality of a dynamical system of claim 7 wherein the step comprising applying individual differences multidimensional scaling further comprises applying nonmetric individual differences multidimensional scaling.

10. A method for determining dimensionality of a dynamical system from partial data, the dimensionality corresponding to a number of degrees of freedom in the dynamical system, the method comprising the steps of:

using a computer system, sampling data from the dynamical system;

using the computer system, mapping the data into one or more matrices;

using the computer system, applying individual differences multidimensional scaling to the one or more matrices to produce rate of change of stress/energy output;

using the computer system, processing the rate of change of stress/energy output to determine dimensionality of the dynamical system.

11. The method determining dimensionality of a dynamical system from partial data of claim 10 wherein the step of mapping the data into one or more matrices comprises a processing step selected from the group consisting of forming one or more symmetric matrices, forming one or more hollow symmetric matrices, entry-wise substituting the received data to populate one or more symmetric matrices, forming one or more proximity matrices, forming one or more distance matrices, and forming one or more Euclidean distance matrices.

12. The method for determining dimensionality of a dynamical system from partial data of claim 10 wherein applying individual differences multidimensional scaling comprises applying nonmetric individual differences multidimensional scaling.