Paper Info
Title
Nonlinear dimension reduction using ISOMap based on class information
Abstract
Image processing and machine learning communities have long addressed the problems involved in the analysis of large high-dimensional data sets. To deal with high-dimensional data efficiently, learning core properties of given data set is important. The manifold learning methods such as ISOMap try to identify a low-dimensional manifold from a set of unorganized samples. ISOMap method is an extension of the classical multidimensional scaling method for dimension reduction, which find a linear subspace in which dissimilarity between data points is preserved. In order to measure dissimilarity, ISOMap uses the geodesic distances on the manifold instead of Euclidean distance. In this paper, we propose a modification of ISOMap using class information, which is often given in company with input data in many applications such as pattern classification. Since the conventional ISOMap does not use class information in approximating true geodesic distance between each pair of data points, it is difficult to construct a data structure related to class-membership that may give important information for given task such as data visualization and classification. The proposed method utilizes class-membership for measuring distance of data pair so as to find a low-dimensional manifold preserving the distance between classes as well as the distance between data points. Through computational experiments on artificial data sets and real facial data sets, we confirm that the proposed method gives better performance than the conventional ISOMap.
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5178988
IJCNN
Keywords
Field
DocType
input data,high-dimensional data,conventional isomap,data visualization,artificial data set,data point,data pair,class information,nonlinear dimension reduction,large high-dimensional data set,data structure,manifold learning,face,euclidean distance,dimension reduction,face recognition,manifolds,multidimensional systems,data mining,geodesic distance,image analysis,learning artificial intelligence,image processing,principal component analysis,multidimensional scaling,computer experiment,machine learning,data analysis,data structures,high dimensional data,graph theory
Data point,Data structure,Data set,Data visualization,Dimensionality reduction,Pattern recognition,Computer science,Euclidean distance,Artificial intelligence,Nonlinear dimensionality reduction,Machine learning,Isomap
Conference
ISSN
Citations 
PageRank 
2161-4393
0
0.34
References 
Authors
7
2
Name
Order
Citations
PageRank
Minkook Cho162.55
Hyeyoung Park219432.70