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Title: Multitask coupled logistic regression and its fast implementation for large multitask datasets
Authors: Gu, X
Chung, FL 
Ishibuchi, H
Wang, S
Keywords: Dual coordinate descent method (CDdual)
Logistic regression (LR)
Multitask classification learning (MTC)
Posterior probability
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2015, v. 45, no. 9, 6964787, p. 1953-1966 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: When facing multitask-learning problems, it is desirable that the learning method could find the correct input-output features and share the commonality among multiple domains and also scale-up for large multitask datasets. We introduce the multitask coupled logistic regression (LR) framework called LR-based multitask classification learning algorithm (MTC-LR), which is a new method for generating each classifier for each task, capable of sharing the commonality among multitask domains. The basic idea of MTC-LR is to use all individual LR based classifiers, each one appropriate for each task domain, but in contrast to other support vector machine (SVM)-based proposals, learning all the parameter vectors of all individual classifiers by using the conjugate gradient method, in a global way and without the use of kernel trick, and being easily extended into its scaled version. We theoretically show that the addition of a new term in the cost function of the set of LRs (that penalizes the diversity among multiple tasks) produces a coupling of multiple tasks that allows MTC-LR to improve the learning performance in a LR way. This finding can make us easily integrate it with a state-of-the-art fast LR algorithm called dual coordinate descent method (CDdual) to develop its fast version MTC-LR-CDdual for large multitask datasets. The proposed algorithm MTC-LR-CDdual is also theoretically analyzed. Our experimental results on artificial and real-datasets indicate the effectiveness of the proposed algorithm MTC-LR-CDdual in classification accuracy, speed, and robustness.
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2014.2362771
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