Title
A new method for performance analysis in nonlinear dimensionality reduction
Abstract
AbstractAbstractIn this paper, we develop a local rank correlation (LRC) measure which quantifies the performance of dimension reduction methods. The LRC is easily interpretable, and robust against the extreme skewness of nearest neighbor distributions in high dimensions. Some benchmark datasets are studied. We find that the LRC closely corresponds to our visual interpretation of the quality of the output. In addition, we demonstrate that the LRC is useful in estimating the intrinsic dimensionality of the original data, and in selecting a suitable value of tuning parameters used in some algorithms.
Year
DOI
Venue
2020
10.1002/sam.11445
Periodicals
Keywords
Field
DocType
dimension reduction,Isomap,local tangent space alignment,manifold,maximum variance unfolding,principal component analysis,rank correlation
Rank correlation,Data mining,Local tangent space alignment,Dimensionality reduction,Pattern recognition,Computer science,Artificial intelligence,Nonlinear dimensionality reduction,Manifold,Principal component analysis,Isomap
Journal
Volume
Issue
ISSN
13
1
1932-1864
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jiaxi Liang100.34
Shoja'eddin Chenouri251.09
Christopher G. Small300.34