Document Type

Conference Paper

Department/Unit

Department of Computer Science

Language

English

Abstract

In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.

Publication Date

9-2017

Source Publication Title

MediaEval 2017

Editors

Gravier, Guillaume ; Bischke, Benjamin ; Demarty, Claire-Hélène ; Zaharieva, Maia ; Riegler, Michael ; Dellandrea, Emmanuel ; Bogdanov, Dmitry ; Sutcliffe, Richard ; Jones, Gareth J.F. ; Larson, Martha

Conference Location

Dublin, Ireland

Publisher

CEUR-WS

Copyright

Copyright © held by the owner/author(s)

Funder

This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.

Link to Publisher's Edition

http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_7.pdf

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