ACTIVE METRIC LEARNING DEVICE, ACTIVE METRIC LEARNING METHOD, AND PROGRAM

• US 20110004578A1
• Filed: 12/08/2008
• Published: 01/06/2011
• Est. Priority Date: 02/22/2008
• Status: Abandoned Application

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Momma, Michinari, Komura, Daisuke, Matsumura, Norikazu, Morinaga, Satoshi
• Application Number:
  US12/918,832
• Publication Number:
  US 20110004578A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A metric application unit receives data under analysis having a plurality of attributes and a metric indicative of the distance between the data under analysis, calculates the distance between the data under analysis, and output and stores a data analysis result which is generated from an analysis on the data under analysis with a predetermined function, using the calculated distance between the data under analysis. A metric optimization unit generates side-information based on an indication of feedback information entered from the outside and including either similarities between the data under analysis, or the attributes, or a combination thereof, generates a metric which complies with a predetermined condition, based on the generated side information, and stores the generated metric in a metric learning result storage unit.

Claims

1. An active metric learning device comprising:

a metric applied data analysis unit including;

a metric application unit that receives data under analysis having a plurality of attributes and a metric for calculating the distance between the data under analysis, and that calculates the distance between the data under analysis;

a data analysis unit that analyzes the data under analysis with a predetermined function using the distance between the data under analysis calculated by said metric application unit, and that outputs a data analysis result generated through the analysis; and

an analysis result storage unit that stores the data analysis result generated by said data analysis unit; and

a metric optimization unit including;

a feedback conversion unit that generates side-information which presents information required for metric learning, based on instructions indicated by feedback information entered from the outside, said feedback information including similarities between the data under analysis stored in said analysis result storage unit or the attributes or a combination thereof; and

a metric learning unit that generates a metric that complies with a predetermined condition based on the side-information generated by said feedback conversion unit, and that stores the generated metric in a metric learning result storage unit,

wherein said metric application unit calculates the distance between the data under analysis using the metric stored in said metric learning result storage unit.

2. The active metric learning device according to claim 1, further comprising an active learning unit that actively learns the data under analysis based on either the data under analysis, or data derived by applying the metric to the data under analysis, or an analysis result which is the result of analyzing the metric, or a combination thereof, and that stores the result of the active learning in an active learning storage unit.

3. The active metric learning device according to claim 1, wherein:

said metric applied data analysis unit comprises;

a dimension conversion unit that applies a dimensional conversion to the analysis result stored in said analysis result storage unit; and

an analysis result output unit that displays the analysis result after said dimension conversion unit has applied the dimensional conversion thereto.

5. The active metric learning device according to claim 1, wherein said side-information generated by said feedback conversion unit includes information indicative of a similarity between sets of the data under analysis, the distance between the data-under-analysis set and the data under analysis, and pair information indicative of the relationship between the data under analysis and another data under analysis.

6. The active metric learning device according to claim 1, wherein said active learning unit comprises:

an active learning processing unit that identifies an attribute correlated to an attribute which has been fed back in the past, based on the data under analysis, or data generated by applying the metric to the data under analysis, or the analysis result, or a combination thereof; and

an active learning result output unit that presents an attribute identified by said active learning processing unit as a candidate for feedback.

7. The active metric learning device according to claim 1, wherein said metric application unit calculates the distance between the data under analysis based on a product of a metric parameter subjected to the metric, and the difference between data under analysis subjected to the calculation of the distance.

8. The active metric learning device according to claim 1, wherein said data analysis unit analyzes the data under analysis using the predetermined function which is a linear conversion of the data under analysis.

9. The active metric learning device according to claim 2, wherein an experiment planning method is used, or a margin is maximized, or a mutual information amount is optimized when the predetermined important data is learned.

12. The active metric learning device according to claim 1, wherein

said feedback conversion unit uses the feedback information which indicates at least one from among:

whether or not a cluster is necessary, whether or not an attribute is necessary, and an adjustment to an inter-cluster distance, when said feedback conversion unit generates the side-information; and

said feedback conversion unit performs at least one from the followings;

when said feedback information indicates that a cluster is necessary, said feedback conversion unit generates the side-information to meet a restrictive condition which dictates that the distance between data having a characteristic attribute of the cluster is reduced;

when said feedback information indicates that a cluster is necessary, said feedback conversion unit generates the side-information to meet a restrictive condition which dictates that the importance degree is increased for a characteristic attribute of the cluster;

when said feedback information indicates that a cluster is not necessary, said feedback conversion unit generates the side-information to meet a restrictive condition which dictates that the importance degree is reduced for a characteristic attribute of the cluster;

when said feedback information indicates that the distance between clusters is adjusted, said feedback conversion unit generates said side-information to meet a restrictive condition which dictates that the distance between the centers of the clusters is adjusted;

when said feedback information indicates that a cluster is divided, said feedback conversion unit generates side-information to identify a plurality of characteristic attributes within the cluster, extract data which includes the attributes, and increase the importance degree for each of the data;

when said feedback information indicates that a cluster is divided, said feedback conversion unit generates side-information to identify a plurality of characteristic attributes within the cluster, extract data which includes the attributes, increase the importance degree for each of the data, and bring the centers of respective sets further away from each other;

when said feedback information indicates that a cluster is divided, said feedback conversion unit generates side-information to again cluster the cluster, and to separate data within a plurality of clusters resulting from the clustering from the center thereof by a smaller distance; and

when said feedback information indicates that a cluster is divided, said feedback conversion unit generates side-information to again cluster the cluster, separates data within a plurality of clusters resulting from the clustering from the center thereof by a smaller distance, and space the centers apart from each other by a larger distance.

21. The active metric learning device according to claim 3, wherein said dimension conversion unit performs the dimension conversion with the use of one of the followings:

a singular value resolution;

a singular value resolution which imposes a constraint to bring the analysis result closer to a conversion result generated by a dimension conversion which has been executed immediately before the dimension conversion;

a non-negative matrix resolution;

a non-negative matrix resolution which imposes a constraint to bring the analysis result closer to a conversion result generated by a dimension conversion which has been executed immediately before the dimension conversion.

25. The active metric learning device according to claim 1, wherein for generating the metric, said metric learning unit solves one of the following:

a positive semi-definite value planning problem using a general library;

a positive semi-definite value planning problem transformed on the basis of labels given to groups that comprise the data under analysis by dividing the positive semi-definite value planning problem into small problems each including a single variable case, and repeatedly executes optimization; and

a positive semi-definite value planning problem with lower ranks given to metric parameters subjected to the metric by dividing the positive semi-definite value planning problem into small problems each including a single variable case, and repeatedly executes optimization.

28. The active metric learning device according to claim 6, wherein said active learning processing unit identifies the correlated attribute based on one of the followings:

a correlation coefficient;

a collocation score;

a mutual information amount; and

a conditional probability.

4. (canceled)

10-11. -11. (canceled)

13-20. -20. (canceled)

22-24. -24. (canceled)

26-27. -27. (canceled)

29-31. -31. (canceled)

32. An active metric learning method comprising:

metric applied data analysis processing including;

metric application processing that receives data under analysis having a plurality of attributes and a metric for calculating the distance between the data under analysis, and that calculates the distance between the data under analysis;

data analysis processing that analyzes the data under analysis with a predetermined function using the distance between the data under analysis calculated by said metric application processing, and that outputs a data analysis result generated through the analysis; and

analysis result storage processing that stores the data analysis result generated by said data analysis processing operation; and

metric optimization processing including;

feedback conversion processing that generates side-information which presents information required for metric learning, based on in instructions indicated by feedback information entered from the outside, said feedback information including similarities between the data under analysis stored through said analysis result storage processing or the attributes or a combination thereof; and

metric learning processing that generates a metric that complies with a predetermined condition based on the side-information generated by said feedback conversion processing, and that stores the generated metric through a metric learning result storage processing operation,

wherein said metric application processing calculates the distance between the data under analysis using the metric stored through said metric learning result storage processing operation.

33. The active metric learning method according to claim 32, further comprising active learning processing that actively learns the data under analysis based on the data under analysis, or data derived by applying the metric to the data under analysis, or an analysis result which is the result of analyzing the metric, or a combination thereof, and that stores the result of the active learning in active learning storage processing.

34. The active metric learning method according to claim 32 wherein

said metric applied data analysis processing comprises:

dimension conversion processing that applies a dimensional conversion to the analysis result stored in said analysis result storage processing; and

analysis result output processing that displays the analysis result after the dimensional conversion has been applied thereto in said dimension conversion processing.

36. The active metric learning method according to claim 32, wherein said side-information generated in said feedback conversion processing includes information indicative of a similarity between sets of the data under analysis, the distance between the data-under-analysis set and the data under analysis, and pair information indicative of the relationship between the data under analysis and other data under analysis.

37. The active metric learning method according to claim 33, by further comprising:

active learning processing that identifies an attribute correlated to an attribute which has been fed back in the past, based on either the data under analysis, or data generated by applying the metric to the data under analysis, or the analysis result, or a combination thereof; and

active learning result output processing that presents an attribute identified by said active learning processing as a candidate for feedback.

38. The active metric learning method according to claim 32, wherein said metric application processing includes calculating the distance between the data under analysis based on the product of a metric parameter subjected to the metric, and the difference between data under analysis subjected to the calculation of the distance.

39. The active metric learning method according to claim 32, wherein said predetermined function used to analyze the data under analysis in said data analysis processing is a linear conversion of the data under analysis.

40. The active metric learning method according to claim 33, wherein an experiment planning is used, or a margin is maximized, or a mutual information amount is optimized when the predetermined important data is learned.

43. The active metric learning method according to claim 32, wherein said feedback conversion processing includes generating the side-information using the feedback information which indicates at least one from among:

whether or not a cluster is necessary, whether or not an attribute is necessary, and an adjustment to an inter-cluster distance, and

said feedback conversion processing performs at least one from the followings;

when said feedback information indicates that a cluster is necessary, said feedback conversion processing includes generating the side-information to meet a restrictive condition which dictates that the distance between data having a characteristic attribute of the cluster is reduced;

when said feedback information indicates that a cluster is necessary, said feedback conversion processing includes generating the side-information to meet a restrictive condition which dictates that the importance degree is increased for a characteristic attribute of the cluster;

when said feedback information indicates that a cluster is not necessary, said feedback conversion processing includes generating the side-information to meet a restrictive condition which dictates that the importance degree is reduced for a characteristic attribute of the cluster;

when said feedback information indicates that the distance between clusters is adjusted, said feedback conversion processing includes generating said side-information to meet a restrictive condition which dictates that the distance between the centers of the clusters is adjusted;

when said feedback information indicates that a cluster is divided, said feedback conversion processing includes generating side-information to identify a plurality of characteristic attributes within the cluster, extract data which includes the attributes, and increase the importance degree for item of data;

when said feedback information indicates that a cluster is divided, said feedback conversion processing includes generating side-information to identify a plurality of characteristic attributes within the cluster, extract data which includes the attributes, increase the importance degree for item of data, and bring the centers of respective sets further away from each other;

when said feedback information indicates that a cluster is divided, said feedback conversion processing includes generating side-information to again cluster the cluster, and separate data within a plurality of clusters resulting from the clustering from the center thereof by a smaller distance; and

when said feedback information indicates that a cluster is divided, said feedback conversion processing includes generating side-information to again cluster the cluster, separate data within a plurality of clusters resulting from the clustering from the center thereof by a smaller distance, and space the centers apart from each other by a larger distance.

52. The active metric learning method according to claim 34, wherein said dimension conversion processing includes performing the dimension conversion with the use of one of the followings:

a singular value resolution;

a singular value resolution which imposes a constraint to bring the analysis result closer to a conversion result generated by a dimension conversion which has been executed immediately before the first dimension conversion;

a non-negative matrix resolution; and

a non-negative matrix resolution which imposes a constraint to bring the analysis result closer to a conversion result generated by a dimension conversion which has been executed immediately before the dimension conversion.

56. The active metric learning method according to claim 32, wherein for generating the metric, said metric learning processing includes one of the followings:

solving a positive semi-definite value planning problem using a general library;

solving a positive semi-definite value planning problem transformed on the basis of labels given to groups comprised of the data under analysis by dividing the positive semi-definite value planning problem into small problems each including a single variable case, and repeatedly executing optimization; and

solving a positive semi-definite value planning problem with lower ranks given to metric parameters subjected to the metric by dividing the positive semi-definite value planning problem into small problems each including a single variable case, and repeatedly executing optimization.

59. The active metric learning method according to claim 37, wherein said active learning processing includes identifying the correlated attribute based on one of the followings:

a correlation coefficient, a collocation score, a mutual information amount, and a conditional probability.

35. (canceled)

41-42. -42. (canceled)

44-51. -51. (canceled)

53-55. -55. (canceled)

57-58. -58. (canceled)

60-62. -62. (canceled)

63. A computer-readable storage medium storing a computer program for causing a computer to execute:

a metric applied data analysis procedure including;

a metric application procedure that receives data under analysis having a plurality of attributes and a metric for calculating the distance between the data under analysis, and that calculates the distance between the data under analysis;

a data analysis procedure that analyzes the data under analysis with a predetermined function using the distance between the data under analysis calculated in said metric application procedure, and that outputs a data analysis result generated through the analysis; and

an metric result storage procedure that stores the data analysis result generated in said data analysis procedure; and

a metric optimization procedure including;

a feedback conversion procedure that generates side-information which presents information required for metric learning, based on instructions indicated by feedback information entered from the outside, said feedback information including similarities between the data under analysis stored through said analysis result storage procedure or the attributes or a combination thereof; and

a metric learning procedure that generates a metric that complies with a predetermined condition based on the side-information generated in said feedback conversion procedure, and that stores the generated metric through a metric learning result storage procedure,

wherein said metric application procedure calculates the distance between the data under analysis using the metric stored through said metric learning result storage procedure.

64. The computer-readable storage medium according to claim 63, further comprising an active learning procedure that actively learns the data under analysis based on the data under analysis, or data derived by applying the metric to the data under analysis, or an analysis result which is the result of analyzing the metric, or a combination thereof, and that stores the result of the active learning in an active learning storage procedure.

65-93. -93. (canceled)