A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems

    Authors

    IEEE Trans. Neural Netw. Learn. Syst.

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Compos. Pt. B-Eng.

    Keywords

    Machine learning; optimization methods; pattern recognition; supervised; learning; support vector machines (SVMs); SUPPORT; SPARSITY; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

    Abstract

    Over the past few years, multiple kernel learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including multitask learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a nontrivial accomplishment. In this paper we present a general multitask multiple kernel learning (MT-MKL) framework that subsumes well-known MT-MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely partially-shared common space MT-MKL, and demonstrate its merits through experimentation.

    Subjects

    C. Li; M. Georgiopoulos;G. C. Anagnostopoulos

    Volume

    25

    Issue/Number

    7

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    1287

    Last Page

    1297

    WOS Identifier

    WOS:000337906300004

    ISSN

    2162-237X

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