Absolute fused lasso and its application to genome-wide association studies

Tao Yang, Jun Liu, Pinghua Gong, Ruiwen Zhang, Xiaotong Shen, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In many real-world applications, the samples/features acquired are in spatial or temporal order. In such cases, the magnitudes of adjacent samples/features are typically close to each other. Meanwhile, in the high-dimensional scenario, identifying the most relevant samples/features is also desired. In this paper, we consider a regularized model which can simultaneously identify important features and group similar features together. The model is based on a penalty called Absolute Fused Lasso (AFL). The AFL penalty encourages sparsity in the coefficients as well as their successive diffierences of absolute values|i.e., local constancy of the coeffcient components in absolute values. Due to the non-convexity of AFL, it is challenging to develop effecient algorithms to solve the optimization problem. To this end, we employ the Difference of Convex functions (DC) programming to optimize the proposed non-convex problem. At each DC iteration, we adopt the proximal algorithm to solve a convex regularized sub-problem. One of the major contributions of this paper is to develop a highly efficient algorithm to compute the proximal operator. Empirical studies on both synthetic and real-world data sets from Genome-Wide Association Studies demonstrate the efficiency and effectiveness of the proposed approach in simultaneous identifying important features and grouping similar features.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1955-1964
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Other

Other22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period8/13/168/17/16

Bibliographical note

Funding Information:
This work was supported in part by research grants from NIH (R01 LM010730 and RF1 AG051710) and NSF (IIS-0953662 and III-1421057)

Keywords

  • Absolute fused lasso
  • GWAS
  • Non-convex optimization
  • Proximal operator

Fingerprint Dive into the research topics of 'Absolute fused lasso and its application to genome-wide association studies'. Together they form a unique fingerprint.

Cite this