Feature selection algorithm under conditions of noisy data and limited recording
View Patent ↗Application of inter-class and intra-class filtering, based on aggregate point-to-point distances, to vector data for purposes of filtering the vector data for purposes of pattern recognition. In some embodiments: (i) the inter-class filtering is based on Euclidean distance, in all dimensions, between vector data points in vector space; and/or (ii) the intra-class filtering is based on a distance, in all dimensions, between vector data points in vector space.
1. A method comprising:
receiving, by machine logic of a computer, a set of vector data, wherein the set of vector data includes a plurality of vector values from each class of a plurality of classes;
applying, by machine logic of the computer, an inter-class filtering to the set of vector data, wherein the inter-class filtering is based, at least in part, on an inter-class distance; and
applying, by machine logic of the computer, an intra-class filtering to the set of vector data, wherein the intra-class filtering is based, at least in part, on an intra-class distance;
wherein:
the inter-class distance is based on a sum of distances between: (i) a subject vector value in the plurality of vector values; and (ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and
the intra-class distance is based on a sum of distances between: (i) a subject vector value in the plurality of vector values in a first class of the plurality of classes; and (ii) at least some of the vector values in the plurality of vector values of the first class.
2. The method of claim 1 , further comprising:
performing, by machine logic of the computer, a pattern recognition related action, wherein the pattern recognition related action is based, at least in part, on the inter-class filtering and the intra-class filtering.
3. The method of claim 2 , wherein the performing, by machine logic of the computer, a pattern recognition related action includes:
assigning, by machine logic of the computer, a pattern to each class in the plurality of classes, wherein the pattern is based on: the inter-class filtering or the intra-class filtering.
4. The method of claim 2 , wherein the performing, by machine logic of the computer, a pattern recognition related action includes:
determining, by machine logic of the computer, a class to which an unknown vector value in the plurality of vector values belongs based, at least in part, on a previous pattern recognition related action.
5. The method of claim 1 , wherein:
the inter-class distance is based on a sum of Euclidean distances between: (i) a subject vector value in the plurality of vector values; and (ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and
the intra-class distance is based on a sum of Euclidean distances between: (i) a subject vector value in the plurality of vector values in the first class of the plurality of classes; and (ii) at least some of the vector values in the plurality of vector values of the first class.
6. The method of claim 1 , further comprising:
applying, by machine logic of the computer, a density estimation to each class of the plurality of classes, wherein the density estimation is based, at least in part, on a determination of a representation for each class of the plurality of classes.
7. The method of claim 1 further comprising:
applying, by machine logic of the computer, a second inter-class filtering to the set of vector data, wherein the second inter-class filtering is based, at least in part, on a second inter-class distance.
8. A computer program product comprising a computer readable storage medium having stored thereon:
first program instructions programmed to receive a set of vector data, wherein the set of vector data includes a plurality of vector values from each class of a plurality of classes;
second program instructions programmed to apply an inter-class filtering to the set of vector data, wherein the inter-class filtering is based, at least in part, on an inter-class distance; and
third program instructions programmed to apply an intra-class filtering to the set of vector data, wherein the intra-class filtering is based, at least in part, on an intra-class distance;
wherein:
the inter-class distance is based on a sum of distances between: (i) a subject vector value in the plurality of vector values; and (ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and
the intra-class distance is based on a sum of distances between: (i) a subject vector value in the plurality of vector values in a first class of the plurality of classes; and (ii) at least some of the vector values in the plurality of vector values of the first class.
9. The computer program product of claim 8 , further comprising:
fourth program instructions programmed to perform a pattern recognition related action, wherein the pattern recognition related action is based, at least in part, on the inter-class filtering and the intra-class filtering.
10. The computer program product of claim 9 , wherein the fourth program instruction programmed to perform a pattern recognition related action includes:
fifth program instruction programmed to assign a pattern to each class in the plurality of classes, wherein the pattern is based on: the inter-class filtering or the intra-class filtering.
11. The computer program product of claim 9 , wherein the fourth program instruction programmed to perform a pattern recognition related action includes:
fifth program instruction programmed to determine a class to which an unknown vector value in the plurality of vector values belongs based, at least in part, on a previous pattern recognition related action.
12. The computer program product of claim 8 , wherein:
the inter-class distance is based on a sum of Euclidean distances between: (i) a subject vector value in the plurality of vector values; and (ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and
the intra-class distance is based on a sum of Euclidean distances between: (i) a subject vector value in the plurality of vector values in the first class of the plurality of classes; and (ii) at least some of the vector values in the plurality of vector values of the first class.
13. The computer program product of claim 8 , further comprising:
fourth program instructions programmed to apply a density estimation to each class of the plurality of classes, wherein the density estimation is based, at least in part, on a determination of a representation for each class of the plurality of classes.
14. A computer system comprising:
a processor set; and
a computer readable storage medium;
wherein:
the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and
the program instructions include:
first program instructions programmed to receive a set of vector data, wherein the set of vector data includes a plurality of vector values from each class of a plurality of classes;
second program instructions programmed to apply an inter-class filtering to the set of vector data, wherein the inter-class filtering is based, at least in part, on an inter-class distance; and
third program instructions programmed to apply an intra-class filtering to the set of vector data, wherein the intra-class filtering is based, at least in part, on an intra-class distance;
wherein:
the inter-class distance is based on a sum of distances between: (i) a subject vector value in the plurality of vector values; and (ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and
the intra-class distance is based on a sum of distances between: (i) a subject vector value in the plurality of vector values in a first class of the plurality of classes; and (ii) at least some of the vector values in the plurality of vector values of the first class.
15. The computer system of claim 14 , further comprising:
fourth program instructions programmed to perform a pattern recognition related action, wherein the pattern recognition related action is based, at least in part, on the inter-class filtering and the intra-class filtering.
16. The computer system of claim 15 , wherein the fourth program instruction programmed to perform a pattern recognition related action includes:
fifth program instruction programmed to assign a pattern to each class in the plurality of classes, wherein the pattern is based on: the inter-class filtering or the intra-class filtering.
17. The computer system of claim 15 , wherein the fourth program instruction programmed to perform a pattern recognition related action includes:
fifth program instruction programmed to determine a class to which an unknown vector value in the plurality of vector values belongs based, at least in part, on a previous pattern recognition related action.
18. The computer system of claim 14 , wherein:
the inter-class distance is based on a sum of Euclidean distances between: (i) a subject vector value in the plurality of vector values; and (ii) at least some of the vector values in the plurality of vector values of at least one other class of the plurality of classes; and
the intra-class distance is based on a sum of Euclidean distances between: (i) a subject vector value in the plurality of vector values in the first class of the plurality of classes; and (ii) at least some of the vector values in the plurality of vector values of the first class.
19. The computer system of claim 14 , further comprising:
fourth program instructions programmed to apply a density estimation to each class of the plurality of classes, wherein the density estimation is based, at least in part, on a determination of a representation for each class of the plurality of classes.
20. The computer system of claim 14 further comprising:
fourth program instructions programmed to apply a second inter-class filtering to the set of vector data, wherein the second inter-class filtering is based, at least in part, on a second inter-class distance.