Paper Info
Title
The Tradeoff Of Accuracy With Different Landmarks With Manifold Learning
Abstract
High-dimensional data such as hyperspectral images contain abundant information of surface radiation. But the massive redundant information makes it complex to be utilized conveniently. To solve this problem, a manifold learning dimensionality reduction framework for hyperspectral image is proposed. Firstly, statistical sampling methods were used to sample a subset of data points as landmarks. A skeleton of the manifold was then identified basing on the landmarks. The remaining data points were then inserted into the skeleton by Locally Linear Embedding algorithm. At last, original data sets and data sets reduced with different manifold learning approaches were classified by KNN classifier to evaluate the performance of the proposed framework. The framework was tested on AVIRIS Salinas-A dataset. The experimental results showed that the tradeoff of accuracy with different landmarks is of great significant. Insufficient landmarks lead to low accuracy and excess landmarks may spend a considerable amount of time.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7729707
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Dimensionality reduction, hyperspectral image, incremental manifold learning
Data point,Computer vision,Data set,Algorithm design,Dimensionality reduction,Pattern recognition,Computer science,Manifold alignment,Hyperspectral imaging,Artificial intelligence,Nonlinear dimensionality reduction,Classifier (linguistics)
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
7
11
Name
Order
Citations
PageRank
Zezhong Zheng12912.43
Chengjun Pu200.34
Mingcang Zhu3151.97
Zhiqin Huang400.68
Yong He501.69
Yicong Feng600.68
Yufeng Lu701.01
Zhenlu Yu801.35
Shengli Wang901.01
Shijie Yu1001.01
jiang li11239.88