Methods and apparatus are provided for estimating anomalous sensors. The apparatus includes a target data acquiring to acquire a plurality of sets of target data serving as an examination target, each set of target data being output by a plurality of sensors. The apparatus further includes a calculating section calculate, for each of a plurality of sensor groups such that each sensor group includes at least two sensors among the plurality of sensors, a degree of difference of a target data distribution of the plurality of sets of target data relative to a reference data distribution of output from the sensor group. The apparatus additionally includes an estimating section to estimate one or more sensors among the plurality of sensors to be a source of outlierness, based on a calculation result of the calculating section.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus comprising: a target data acquiring section operable to acquire a plurality of sets of target data serving as an examination target, each set of target data output by a plurality of sensors; a calculating section operable to calculate, for each of a plurality of sensor groups such that each sensor group includes at least two sensors among the plurality of sensors, a degree of difference of a target data distribution of the plurality of sets of target data relative to a reference data distribution of output from each sensor group; and an estimating section operable to estimate one or more sensors among the plurality of sensors to be a source of outlierness, based on a calculation result of the calculating section. 2. The apparatus according to claim 1, wherein the degree of difference is based on a KL-divergence between the target data distribution and the reference data distribution. 3. The apparatus according to claim 2, wherein the calculating section is further operable to calculate a KL-divergence from a distribution model of the target data distribution and a distribution model of the reference data distribution. 4. The apparatus according to claim 2, wherein the calculating section is further operable to estimate a KL-divergence between the target data distribution and the reference data distribution based on the each set of target data from the sensor group and each sets of reference data from the sensor group. 5. The apparatus according to claim 1, wherein the plurality of sets of target data is output sequentially from the plurality of sensors. 6. The apparatus according to claim 5, wherein the target data acquiring section is operable to acquire the plurality of sets of target data output from the plurality of sensors in a time period. 7. The apparatus according to claim 6, wherein: the target data acquiring section is operable to acquire two or more sequences of the plurality of sets of target data output from the plurality of sensors in two or more overlapping time periods; the calculating section is operable to calculate the degree of difference for each of the two or more sequences of the plurality of sets of target data; and the estimating section is operable to estimate one or more of sensors to be a source of outlierness based on the calculation result for each of the two or more sequences of the plurality of sets of target data. 8. The apparatus according to claim 1, wherein: the calculating section is further operable to calculate, for each of the plurality of sensors, the degree of difference of each sensor group including the sensor; and the estimating section is further operable to calculate, for each sensor, a degree of association with which the sensor is associated with the difference, from the at least two degrees of difference calculated for the groups including the sensor, and estimate whether the sensor is a source of outlierness based on the degree of association calculated for each sensor. 9. The apparatus according to claim 8, wherein the estimating section is further operable to estimate each sensor having a degree of association that is greater than or equal to a reference value. 10. The apparatus according to claim 8, wherein the estimating section is further operable to: sequentially select sensors to be sources of outlierness, in order of degree of association beginning with a sensor having the highest degree of association; and estimate that sensors that have not yet been selected are not sources of outlierness, if a difference between the degree of association of the most recently selected sensor and the next highest degree of association is greater than or equal to a reference difference. 11. The apparatus according to claim 10, wherein: the estimating section is further operable to determine whether to further select a sensor, based on a total or an average of the degrees of difference calculated for sensor groups that do not include sensors that have already been selected. 12. The apparatus according to claim 8, wherein the estimating section is further operable to sequentially select a predetermined number of sensors to be sources of outlierness. 13. The apparatus according to claim 8, wherein: the estimating section is further operable to calculate, for each sensor that has not been selected, the degree of association from the degrees of difference calculated for sensor groups that do not includes sensors that have already been selected. 14. The apparatus according to claim 8, wherein the estimating section is further operable to: sequentially select sensors to not be sources of outlierness, in order of degree of association beginning with a sensor having the lowest degree of association; and estimate that sensors that have not yet been selected are sources of outlierness, if a difference between the degree of association of the most recently selected sensor and a next lowest degree of association is greater than or equal to a reference difference. 15. The apparatus according to claim 14, wherein the estimating section is further operable to calculate, for each sensor that has not been selected, the degree of association from the degrees of difference calculated for sensor groups that do not include sensors that have already been selected. 16. The apparatus according to claim 8, wherein the estimating section is further operable to sequentially select a predetermined number of sensors not to be sources of outlierness, in order of degree of association beginning with a sensor having a lowest degree of association. 17. The apparatus according to claim 1, further comprising: a reference data acquiring section operable to acquire a plurality of sets of reference data serving as a reference for output of the plurality of sensors; and a distribution generating section operable to generate, for each of the plurality of sensor groups, the reference data distribution of output from the sensor group. 18. The apparatus according to claim 1, wherein the calculating section is operable to calculate the degree of difference for each sensor group including two sensors among the plurality of sensors. 19. A method comprising: acquiring a plurality of sets of target data serving as an examination target, each set of target data output by a plurality of sensors; calculating, for each of a plurality of sensor groups such that each sensor group includes at least two sensors among the plurality of sensors, a degree of difference of a target data distribution of the plurality of sets of target data relative to a reference data distribution of output from each sensor group; and estimating one or more sensors to be a source of outlierness, based on a calculation result of the degree of difference of the target data. 20. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising: acquiring a plurality of sets of target data serving as an examination target, each set of target data output by a plurality of sensors; calculating, for each of a plurality of sensor groups such that each sensor group includes at least two sensors among the plurality of sensors, a degree of difference of a target data distribution of the plurality of sets of target data relative to a reference data distribution of output from each sensor group; and estimating one or more sensors to be a source of outlierness, based on a calculation result of the degree of difference of the target data.