2012 EfficientEvaluationofLargeSeque

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Notes

Classification of sequences drawn from a finite alphabet using a family of string kernels with inexact matching (e.g., spectrum or mismatch) has shown great success in machine learning. However, selection of optimal mismatch kernels for a particular task is severely limited by inability to compute such kernels for long substrings (k - mers) with potentially many mismatches (m). In this work we introduce a new method that allows us to exactly evaluate kernels for large k, m and arbitrary alphabet size. The task can be accomplished by first solving the more tractable problem for small alphabets, and then trivially generalizing to any alphabet using a small linear system of equations. This makes it possible to explore a larger set of kernels with a wide range of kernel parameters, opening a possibility to better model selection and improved performance of the string kernels. To investigate the utility of large (k, m) string kernels, we consider several sequence classification problems, including protein remote homology detection, fold prediction, and music classification. Our results show that increased k - mer lengths with larger substitutions can improve classification performance.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 EfficientEvaluationofLargeSeque	Pavel P. Kuksa Vladimir Pavlovic			Efficient Evaluation of Large Sequence Kernels				10.1145/2339530.2339649		2012