NDLI: An Improved Manifold Learning Algorithm for Data Visualization

Content Provider	IEEE Xplore Digital Library
Author	Rui-Jun Gu Wen-Bo Xu
Copyright Year	2006
Description	Author affiliation: Sch. of Inf. Technol., Southern Yangtze Univ., Wuxi Jiangsu (Rui-Jun Gu; Wen-Bo Xu)
Abstract	Recently, a series of methods called manifold learning have been developed to visualize the convex but intrinsically flat manifolds such as Swiss roll. Isomap is a representative of them, which can easily discover low dimensional manifolds from high dimensional data but its computation complexity is quadratic. To speed up Isomap, L-Isomap was proposed to reduce the complexity by using landmark points. But how to select landmarks is an open problem. In this paper, we present an extension of Isomap focusing on the suitable selection of landmarks even the number of landmarks is quite small. In our method, each data point is assigned a weight according to the distance between it and its neighbors and point with a higher weight has a larger probability to be selected as a landmark point. The selection of landmarks falls into two phases. In 1st phase, n' candidate landmarks are selected only by the weights of data points. And in 2nd phase, n landmarks are refined from the candidates by maximizing the sum of distances between all pairwise landmarks. Experimental results showed that our method was more stable than L-Isomap and outperformed L-Isomap especially when the number of landmark points is quite small
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	1170
Ending Page	1173
File Size	297274
Page Count	4
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258599
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data visualization Euclidean distance Manifolds Information technology Data mining Data analysis Principal component analysis Laplace equations Lighting control Linear approximation Isomap dimensionality reduction manifold learning
Content Type	Text
Resource Type	Article

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