Paper Info
Title
Semi-supervised adaptive feature analysis and its application for multimedia understanding.
Abstract
Multimedia understanding for high dimensional data is still a challenging work, due to redundant features, noises and insufficient label information it contains. Graph-based semi-supervised feature learning is an effective approach to address this problem. Nevertheless, Existing graph-based semi-supervised methods usually depend on the pre-constructed Laplacian matrix but rarely modify it in the subsequent classification tasks. In this paper, an adaptive local manifold learning based semi-supervised feature selection is proposed. Compared to the state-of-the-art, the proposed algorithm has two advantages: 1) Adaptive local manifold learning and feature selection are integrated jointly into a single framework, where both the labeled and unlabeled data are utilized. Besides, the correlations between different components are also considered. 2) A group sparsity constraint, i.e. -norm, is imposed to select the most relevant features. We also apply the proposed algorithm to serval kinds of multimedia understanding applications. Experimental results demonstrate the effectiveness of the proposed algorithm.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11042-017-4990-5
Multimedia Tools Appl.
Keywords
Field
DocType
feature selection,semi-supervised learning,adaptive learning,image annotation,3D human action recognition
Data mining,Semi-supervised learning,Feature selection,Computer science,Artificial intelligence,Nonlinear dimensionality reduction,Computer vision,Laplacian matrix,Pattern recognition,Feature (computer vision),Multimedia,Adaptive learning,Pattern recognition (psychology),Machine learning,Feature learning
Journal
Volume
Issue
ISSN
77
3
1380-7501
Citations 
PageRank 
References 
2
0.36
32
Authors
5
Name
Order
Citations
PageRank
Xiaodong Wang1355.19
Rung-Ching Chen233137.37
Fei Yan3289.01
Zhiqiang Zeng413916.35
Chaoqun Hong532413.19