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 language||English (US)|
|Title of host publication||KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Aug 13 2016|
|Event||22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States|
Duration: Aug 13 2016 → Aug 17 2016
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Other||22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016|
|Period||8/13/16 → 8/17/16|
- Absolute fused lasso
- Non-convex optimization
- Proximal operator
Absolute fused lasso and its application to genome-wide association studies. / Yang, Tao; Liu, Jun; Gong, Pinghua; Zhang, Ruiwen; Shen, Xiaotong; Ye, Jieping.KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2016. p. 1955-1964 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution