Multiple task transfer learning with small sample sizes. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Saha, Budhaditya; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha
• Is Part Of: Knowledge and information systems. Volume 46:Issue 2(2016:Feb.)
• Page Start: 315
• Page End: 342
• Publication Date: 2016-02
• Subjects: Multi-task -- Transfer learning -- Optimization -- Healthcare -- Data mining -- Statistical analysis; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0821-z
• Languages: English
• ISSNs: 0219-1377

Abstract

Abstract Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data withsmall numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. Thedegree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.