A method of managing memory usage of a stored training set for classification includes calculating one or both of a first similarity metric and a second similarity metric. The first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample. The second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample. The method also includes selectively storing the new training sample in memory based on the first similarity metric, and/or the second similarity metric.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of managing memory usage of a stored training set for classification, comprising: calculating at least one of a first similarity metric or a second similarity metric, wherein the first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample, and wherein the second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample; and selectively storing the new training sample in memory based at least in part on the at least one of the first similarity metric or the second similarity metric. 2. The method of claim 1, further comprising: calculating at least one of a third similarity metric or a fourth similarity metric, wherein the third similarity metric is associated with a candidate training sample and existing training samples of a same class as the candidate training sample, and wherein the fourth similarity metric is associated with the candidate training sample and existing training samples of a different class than the candidate training sample; and selectively removing the candidate training sample from the memory based at least in part on the at least one of the third similarity metric or the fourth similarity metric. 3. The method of claim 1, wherein at least one of the first similarity metric or the second similarity metric are computed based at least in part on a centroid of the existing training samples. 4. The method of claim 1, in which the first similarity metric comprises a difference between: a minimum distance between any two points of the existing training samples in the same class, and a minimum distance between points of the new training sample and all existing training samples in the same class. 5. The method of claim 1, in which the first similarity metric comprises a difference between: a maximum summed distance between any two points of the existing training samples in the same class, and a maximum summed distance between points of the new training sample and all of the existing training samples in the same class. 6. The method of claim 1, in which the first similarity metric comprises a maximum summed distance between each point of the existing training samples in a class and all existing training samples in other classes, and the second similarity metric comprises a maximum summed distance between points of the new training sample in the class and all existing training samples in the other classes, the new training sample being selectively added to the training set based at least in part on a difference between the first similarity metric and the second similarity metric. 7. The method of claim 1, further comprising receiving new training samples. 8. An apparatus for managing memory usage of a stored training set for classification, comprising: means for calculating at least one of a first similarity metric or a second similarity metric, wherein the first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample, and wherein the second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample; and means for selectively storing the new training sample in memory based at least in part on the at least one of the first similarity metric or the second similarity metric. 9. The apparatus of claim 8, further comprising: means for calculating at least one of a third similarity metric or a fourth similarity metric, wherein the third similarity metric is associated with a candidate training sample and existing training samples of a same class as the candidate training sample, and wherein the fourth similarity metric is associated with the candidate training sample and existing training samples of a different class than the candidate training sample; and means for selectively removing the candidate sample from the memory based at least in part on the at least one of the third similarity metric or the fourth similarity metric. 10. The apparatus of claim 8, wherein at least one of the first similarity metric or the second similarity metric are computed based at least in part on a centroid of the existing training samples. 11. The apparatus of claim 8, in which the first similarity metric comprises a difference between: a minimum distance between any two points of the existing training samples in the same class, and a minimum distance between points of the new training sample and all existing training samples in the same class. 12. The apparatus of claim 8, in which the first similarity metric comprises a difference between: a maximum summed distance between any two points of the existing training samples in the same class, and a maximum summed distance between points of the new training sample and all of the existing training samples in the same class. 13. The apparatus of claim 8, in which the first similarity metric comprises a maximum summed distance between each point of the existing training samples in a class and all existing training samples in other classes, and the second similarity metric comprises a maximum summed distance between points of the new training sample in the class and all existing training samples in the other classes, the new training sample being selectively added to the training set based at least in part on a difference between the first similarity metric and the second similarity metric. 14. The apparatus of claim 8, further comprising receiving new training samples. 15. An apparatus for managing memory usage of a stored training set for classification, comprising: a memory; and at least one processor coupled to the memory and configured: to calculate at least one of a first similarity metric or a second similarity metric, wherein the first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample, and wherein the second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample; and to selectively store the new training sample in the memory based at least in part on the at least one of the first similarity metric or the second similarity metric. 16. The apparatus of claim 15, in which the at least one processor is further configured: to calculate at least one of a third similarity metric or a fourth similarity metric, wherein the third similarity metric is associated with a candidate training sample and existing training samples of a same class as the candidate training sample, and wherein the fourth similarity metric is associated with the candidate training sample and existing training samples of a different class than the candidate training sample; and to selectively remove the candidate sample from the memory based at least in part on the at least one of the third similarity metric or the fourth similarity metric. 17. The apparatus of claim 15, wherein the at least one processor is further configured to calculate at least one of the first similarity metric or the second similarity metric based at least in part on a centroid of the existing training samples. 18. The apparatus of claim 15, in which the first similarity metric comprises a difference between: a minimum distance between any two points of the existing training samples in the same class, and a minimum distance between points of the new training sample and all existing training samples in the same class. 19. The apparatus of claim 15, in which the first similarity metric comprises a difference between: a maximum summed distance between any two points of the existing training samples in the same class, and a maximum summed distance between points of the new training sample and all of the existing training samples in the same class. 20. The apparatus of claim 15, in which the first similarity metric comprises a maximum summed distance between each point of the existing training samples in a class and all existing training samples in other classes, and the second similarity metric comprises a maximum summed distance between points of the new training sample in the class and all existing training samples in the other classes, the at least one processor being configured to selectively store the new training sample in the memory based at least in part on a difference between the first similarity metric and the second similarity metric. 21. The apparatus of claim 15, in which the at least one processor is further configured to receive new training samples. 22. A non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to calculate at least one of a first similarity metric or a second similarity metric, wherein the first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample, and wherein the second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample; and program code to selectively store the new training sample in memory based at least in part on the at least one of the first similarity metric or the second similarity metric. 23. The computer-readable medium of claim 22, further comprising: program code to calculate at least one of a third similarity metric or a fourth similarity metric, wherein the third similarity metric is associated with a candidate training sample and existing training samples of a same class as the candidate training sample, and wherein the fourth similarity metric is associated with the candidate training sample and existing training samples of a different class than the candidate training sample; and program code to selectively remove the candidate sample from the memory based at least in part on the at least one of the third similarity metric or the fourth similarity metric. 24. The computer-readable medium of claim 22, further comprising program code to calculate at least one of the first similarity metric or the second similarity metric based at least in part on a centroid of the existing training samples. 25. The computer-readable medium of claim 22, in which the first similarity metric comprises a difference between: a minimum distance between any two points of the existing training samples in the same class, and a minimum distance between points of the new training sample and all existing training samples in the same class. 26. The computer-readable medium of claim 22, in which the first similarity metric comprises a difference between: a maximum summed distance between any two points of the existing training samples in the same class, and a maximum summed distance between points of the new training sample and all of the existing training samples in the same class. 27. The computer-readable medium of claim 22, in which the first similarity metric comprises a maximum summed distance between each point of the existing training samples in a class and all existing training samples in other classes, and the second similarity metric comprises a maximum summed distance between points of the new training sample in the class and all existing training samples in the other classes, and further comprising program code to selectively store the new training sample in the memory based at least in part on a difference between the first similarity metric and the second similarity metric. 28. The computer-readable medium of claim 22, further comprising program code to receive new training samples.