2011 BoundedCoordinateDescentforBiol

• (Ifrim & Wiuf, 2011) ⇒ Georgiana Ifrim, and Carsten Wiuf. (2011). “Bounded Coordinate-descent for Biological Sequence Classification in High Dimensional Predictor Space.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020519

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Bounded+Coordinate-descent+for+Biological+Sequence+Classification+in+High+Dimensional+Predictor+Space
• http://dl.acm.org/citation.cfm?id=2020408.2020519&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; experimentation; greedy coordinate-descent; learning; logistic regression; performance; sequence classification; string classification; support vector machines

Abstract

We present a framework for discriminative sequence classification where linear classifiers work directly in the explicit high-dimensional predictor space of all subsequences in the training set (as opposed to kernel-induced spaces). This is made feasible by employing a gradient-bounded coordinate-descent algorithm for efficiently selecting discriminative subsequences without having to expand the whole space. Our framework can be applied to a wide range of loss functions, including binomial log-likelihood loss of logistic regression and squared hinge loss of support vector machines. When applied to protein remote homology detection and remote fold recognition, our framework achieves comparable performance to the state-of-the-art (e.g., kernel support vector machines). In contrast to state-of-the-art sequence classifiers, our models are simply lists of weighted discriminative subsequences and can thus be interpreted and related to the biological problem - a crucial requirement for the bioinformatics and medical communities.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 BoundedCoordinateDescentforBiol	Georgiana Ifrim Carsten Wiuf			Bounded Coordinate-descent for Biological Sequence Classification in High Dimensional Predictor Space				10.1145/2020408.2020519		2011