Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model

Budhaditya Saha, Sunil Gupta, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Mortality prediction of rare cancer types with a small number of high-dimensional samples is a challenging task. We propose a transfer learning model where both classes in rare cancers (target task) are modeled in a joint framework by transferring knowledge from the source task. The knowledge transfer is at the data level where only 'related' data points are chosen to train the target task. Moreover, both positive and negative class in training enhances the discrimination power of the proposed framework. Overall, this approach boosts the generalization performance of target task with a small number of data points. The formulation of the proposed framework is convex and expressed as a primal problem. We convert this to a dual problem and efficiently solve by alternating direction multipliers method. Our experiments with both synthetic and three real-world datasets show that our framework outperforms state-of-the-art single-task, multi-task, and transfer learning baselines.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR 2016)
EditorsLarry Davis, Alberto Del Bimbo , Brian C. Lovell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509048472
ISBN (Print)9781509048489
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference on Pattern Recognition 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23rd (Proceedings)


ConferenceInternational Conference on Pattern Recognition 2016
Abbreviated titleICPR 2016
Internet address

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