2014 FactorizedSparseLearningModelsw

• (Purushotham et al., 2014) ⇒ Sanjay Purushotham, Martin Renqiang Min, C.-C. Jay Kuo, and Rachel Ostroff. (2014). “Factorized Sparse Learning Models with Interpretable High Order Feature Interactions.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623747

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Factorized+Sparse+Learning+Models+with+Interpretable+High+Order+Feature+Interactions
• http://dl.acm.org/citation.cfm?id=2623330.2623747&preflayout=flat#citedby

Quotes

Author Keywords

• Feature selection; learning; sparse learning

Abstract

Identifying interpretable discriminative high-order feature interactions given limited training data in high dimensions is challenging in both machine learning and data mining. In this paper, we propose a factorization based sparse learning framework termed FHIM for identifying high-order feature interactions in linear and logistic regression models, and study several optimization methods for solving them. Unlike previous sparse learning methods, our model FHIM recovers both the main effects and the interaction terms accurately without imposing tree-structured hierarchical constraints. Furthermore, we show that FHIM has oracle properties when extended to generalized linear regression models with pairwise interactions. Experiments on simulated data show that FHIM outperforms the state-of-the-art sparse learning techniques. Further experiments on our experimentally generated data from patient blood samples using a novel SOMAmer (Slow Off-rate Modified Aptamer) technology show that, FHIM performs blood-based cancer diagnosis and bio-marker discovery for Renal Cell Carcinoma much better than other competing methods, and it identifies interpretable block-wise high-order gene interactions predictive of cancer stages of samples. A literature survey shows that the interactions identified by FHIM play important roles in cancer development.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 FactorizedSparseLearningModelsw	Sanjay Purushotham Martin Renqiang Min C.-C. Jay Kuo Rachel Ostroff			Factorized Sparse Learning Models with Interpretable High Order Feature Interactions				10.1145/2623330.2623747		2014