Paper Info
Title
Semi-supervised manifold learning of time-series hyperspectral forest images
Abstract
The accuracy of regression based on hyperspectral data is degraded by a restricted number of labeled data and the curse of dimensionality inherent in the high-dimensional feature space. In this paper, we propose two types of semi-supervised manifold learning methods for regression by a combination of supervised learning based on a small number of labeled data and unsupervised learning based on abundant unlabeled feature data. The regression and nonlinear manifold learning are realized by a kernelization of generalized eigenvalue problems. The proposed methods are applied to synthetic manifold learning problems and time-series hyperspectral leaf-scale images of oak trees.
Year
DOI
Venue
2014
10.1109/WHISPERS.2014.8077505
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Keywords
Field
DocType
Hyperspectral data,semi-supervised regression,manifold learning,forest phenology,kernel trick
Feature vector,Semi-supervised learning,Pattern recognition,Supervised learning,Curse of dimensionality,Manifold alignment,Unsupervised learning,Artificial intelligence,Nonlinear dimensionality reduction,Machine learning,Mathematics,Feature data
Conference
ISSN
ISBN
Citations 
2158-6268
978-1-4673-9013-2
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Kuniaki Uto13210.40