PUlasso: High-Dimensional Variable Selection With Presence-Only Data

In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The com...

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Veröffentlicht in:Journal of the American Statistical Association Jg. 115; H. 529; S. 334 - 347
Hauptverfasser: Song, Hyebin, Raskutti, Garvesh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis 02.01.2020
Taylor & Francis Ltd
Schlagworte:
Algorithms
Biochemistry
Classification
Computer simulation
Feature selection
Majorization-minimization
Minimax technique
Minimization
Neighborhoods
Nonconvexity
Optimization
PU-learning
Regression analysis
Regularization
Sparsity
Statistical methods
Statistics
World problems
Regularization
Majorization-minimization
Nonconvexity, PU-learning
ISSN:0162-1459, 1537-274X
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Zusammenfassung:In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The combination of presence-only responses and high dimensionality presents both statistical and computational challenges. In this article, we develop the PUlasso algorithm for variable selection and classification with positive and unlabeled responses. Our algorithm involves using the majorization-minimization framework which is a generalization of the well-known expectation-maximization (EM) algorithm. In particular to make our algorithm scalable, we provide two computational speed-ups to the standard EM algorithm. We provide a theoretical guarantee where we first show that our algorithm converges to a stationary point, and then prove that any stationary point within a local neighborhood of the true parameter achieves the minimax optimal mean-squared error under both strict sparsity and group sparsity assumptions. We also demonstrate through simulations that our algorithm outperforms state-of-the-art algorithms in the moderate p settings in terms of classification performance. Finally, we demonstrate that our PUlasso algorithm performs well on a biochemistry example. Supplementary materials for this article are available online.
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ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2018.1546587