Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks

In this paper, we propose a novel semisupervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications. Instead of independently computing the importance of features for each task, our algorithm leverages shared knowledge from multi...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems 2017-10, Vol.28 (10), p.2294-2305
Main Authors:	Chang, Xiaojun, Yang, Yi
Format:	Article
Language:	eng
Subjects:	3-D motion data analysis Algorithm design and analysis Algorithms Correlation Correlation analysis Data mining gene pattern recognition image annotation Machine learning Manifolds Manifolds (mathematics) Multimedia Multimedia communication multitask feature selection Objective function Semisupervised learning State of the art Training Training data
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Summary:	In this paper, we propose a novel semisupervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications. Instead of independently computing the importance of features for each task, our algorithm leverages shared knowledge from multiple related tasks, thus improving the performance of feature selection. Note that the proposed algorithm is built upon an assumption that different tasks share some common structures. The proposed algorithm selects features in a batch mode, by which the correlations between various features are taken into consideration. Besides, considering the fact that labeling a large amount of training data in real world is both time-consuming and tedious, we adopt manifold learning, which exploits both labeled and unlabeled training data for a feature space analysis. Since the objective function is nonsmooth and difficult to solve, we propose an iteractive algorithm with fast convergence. Extensive experiments on different applications demonstrate that our algorithm outperforms the other state-of-the-art feature selection algorithms.
ISSN:	2162-237X 2162-2388