Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer’s Disease


  • Bo Xin Peking University
  • Yoshinobu Kawahara Osaka University
  • Yizhou Wang Peking University
  • Wen Gao Peking University




generalized fused lasso, Alzhemier's disease, parametric graph-cut


Generalized fused lasso (GFL) penalizes variables with L1 norms based both on the variables and their pairwise differences. GFL is useful when applied to data where prior information is expressed using a graph over the variables. However, the existing GFL algorithms incur high computational costs and they do not scale to high-dimensional problems. In this study, we propose a fast and scalable algorithm for GFL. Based on the fact that fusion penalty is the Lov'asz extension of a cut function, we show that the key building block of the optimization is equivalent to recursively solving parametric graph-cut problems. Thus, we use a parametric flow algorithm to solve GFL in an efficient manner. Runtime comparisons demonstrated a significant speed-up compared with the existing GFL algorithms. By exploiting the scalability of the proposed algorithm, we formulated the diagnosis of Alzheimer's disease as GFL. Our experimental evaluations demonstrated that the diagnosis performance was promising and that the selected critical voxels were well structured i.e., connected, consistent according to cross-validation and in agreement with prior clinical knowledge.




How to Cite

Xin, B., Kawahara, Y., Wang, Y., & Gao, W. (2014). Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer’s Disease. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8977



Main Track: Novel Machine Learning Algorithms