Annealed sparsity via adaptive and dynamic shrinking

Kai Zhang, Shandian Zhe, Chaoran Cheng, Zhi Wei, Zhengzhang Chen, Haifeng Chen, Guofei Jiang, Yuan Qi, Jieping Ye

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

5 Scopus citations

Abstract

Sparse learning has received tremendous amount of interest in high-dimensional data analysis due to its model interpretability and the low-computational cost. Among the various techniques, adaptive 1-regularization is an effective framework to improve the convergence behaviour of the LASSO, by using varying strength of regularization across different features. In the meantime, the adaptive structure makes it very powerful in modelling grouped sparsity patterns as well, being particularly useful in high-dimensional multi-task problems. However, choosing an appropriate, global regularization weight is still an open problem. In this paper, inspired by the annealing technique in material science, we propose to achieve "annealed sparsity" by designing a dynamic shrinking scheme that simultaneously optimizes the regularization weights and model coefficients in sparse (multi-task) learning. The dynamic structures of our algorithm are twofold. Feature-wise ("spatially"), the regularization weights are updated interactively with model coefficients, allowing us to improve the global regularization structure. Iteration-wise ("temporally"), such interaction is coupled with gradually boosted ℓ1-regularization by adjust- ing an equality norm-constraint, achieving an "annealing" effect to further improve model selection. This renders interesting shrinking behaviour in the whole solution path. Our method competes favorably with state-of-the-art methods in sparse (multi-task) learning. We also apply it in expression quantitative trait loci analysis (eQTL), which gives useful biological insights in human cancer (melanoma) study.

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
Pages1325-1334
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
Country/TerritoryUnited States
CitySan Francisco
Period8/13/168/17/16

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Adaptive LASSO
  • Multi-task LASSO
  • Reg-ularization path
  • Sparse multi-task learning
  • Sparse regression

Fingerprint

Dive into the research topics of 'Annealed sparsity via adaptive and dynamic shrinking'. Together they form a unique fingerprint.

Cite this