An information processing system, a computer readable storage medium, and a method for comparing training data with test data. The method can include collecting by a processor of a machine learning system, training data having meta-data information used for training the machine learning system, and test data lacking meta-data information. The method can further include training the machine learning system with the training data, extracting components of the machine learning system from analysis of the training data to provide a training data extraction, extracting components of the machine learning system from analysis of the test data to provide a test data extraction, performing at least a low-dimensional comparison of the training data extraction with the test data extraction using a statistical comparison technique, and generating meta-data information for the test data when the low-dimensional comparison meets or exceeds a predetermined threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: collecting by at least one processor of at least one computing device of a machine learning system, training data having meta-data information used for training the machine learning system; collecting by the at least one processor, test data lacking meta-data information; training the machine learning system with the training data; extracting components of the machine learning system from analysis of the training data to provide a training data extraction; extracting components of the machine learning system from analysis of the test data to provide a test data extraction; performing at least a low-dimensional comparison of the training data extraction with the test data extraction using a statistical comparison technique; and generating meta-data information for the test data when the at least the low-dimensional comparison meets or exceeds a predetermined threshold. 2. The method of claim 1, further comprising presenting the low-dimensional comparison of the training data extraction with the test data extraction on a user interface. 3. The method of claim 1, wherein the training data extraction and the test data extraction each have multiple components and the low-dimensional comparison generates a numerical distance between predetermined components of the machine learning system of the training data extraction and the test data extraction. 4. The method of claim 1, wherein the training data extraction and the test data extraction each have multiple components and each of the multiple components are normalized before performing the low dimensional comparison. 5. The method of claim 1, wherein the low-dimensional comparison is at least a pairwise dimensional comparison. 6. The method of claim 1, wherein the predetermined threshold is a number in a range between 0 and 1 indicating how similar the training data extraction is to the test data extraction. 7. The method of claim 1, wherein the statistical comparison technique uses a Jensen-Shannon Divergence. 8. The method of claim 1, wherein the training data comprises an image having at least one of objects or concepts represented by the image and further including corresponding meta-data representing the objects or concepts. 9. The method of claim 1, wherein the step of performing the at least the pairwise dimensional comparison is a penultimate step providing weighted components as an input to a final decision output node. 10. The method of claim 1, wherein the pairwise dimensional comparison provides a predetermined feature relationship between predetermined components of training data extraction and the test data extraction providing a higher percentage of certainty of an accurate result, relative to without using the pairwise dimensional comparison. 11. A system comprising: at least one memory; and at least one processor of a machine learning system communicatively coupled to the at least one memory, the at least one processor, responsive to instructions stored in memory, being configured to perform a method comprising: collecting training data having meta-data information used for training the machine learning system; collecting test data lacking meta-data information; training the machine learning system with the training data; extracting components of the machine learning system from analysis of the training data to provide a training data extraction; extracting components of the machine learning system from analysis of the test data to provide a test data extraction; performing at least a low-dimensional comparison of the training data extraction with the test data extraction using a statistical comparison technique; and generating meta-data information for the test data when the at least the pairwise dimensional comparison meets or exceeds a predetermined threshold. 12. The system of claim 11, further comprising a user interface for presenting the low-dimensional comparison of the training data extraction with the test data extraction. 13. The system of claim 11, wherein the training data comprises an image having at least one of objects or concepts represented by the image and further including corresponding meta-data representing the objects or concepts. 14. The system of claim 11, wherein the training data comprises audio having features represented by the audio and further including corresponding meta-data representing the features. 15. The system of claim 11, wherein the training data extraction and the test data extraction each have multiple features and the analysis produces corresponding histograms for each of the features of the training data extraction and test data extraction. 16. The system of claim 15, wherein the low-dimension comparison is done by a comparison of the histograms of corresponding features of the training data extraction and the test data extraction, and wherein the system further comprising a user interface for presenting by displaying at least one of: the differences compared between features of the training data extraction and corresponding features of the test data extraction; and identification of at least one feature that created the largest difference between the features of the training data extraction and corresponding features of the test data extraction. 17. The system of claim 11, wherein the training data extraction and the test data extraction each have multiple components and each of the multiple components are normalized before performing the low-dimensional comparison. 18. The system of claim 11, wherein the low-dimensional comparison is at least a pairwise dimensional comparison. 19. The system of claim 11, wherein the statistical comparison technique uses a Jensen-Shannon Divergence providing a result in a range between 0 and 1 where 0 signifies zero differences and 1 signifies a maximal difference and alternatively where 0 signifies the maximal difference and 1 signifies zero differences in the comparison. 20. A non-transitory computer-readable medium having stored therein instructions which, when executed by at least one processor, cause a machine learning system to perform a method comprising: collecting by the at least one processor of the machine learning system, training data having meta-data information used for training the machine learning system; collecting by the at least one processor, test data lacking meta-data information; training the machine learning system with the training data; extracting components of the machine learning system from analysis of the training data to provide a training data extraction; extracting components of the machine learning system from analysis of the test data to provide a test data extraction; performing at least a pairwise dimensional comparison of the training data extraction with the test data extraction using a statistical comparison technique; and generating meta-data information for the test data when the at least the pairwise dimensional comparison meets or exceeds a predetermined threshold.