Evolving insight into high-dimensional data

Authors:
Yiqing Tu;Gang Li;Honghua Dai
Affiliations:
School of Information Technology, Deakin University, Vic, Australia;School of Information Technology, Deakin University, Vic, Australia;School of Information Technology, Deakin University, Vic, Australia
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

ISOMap is a popular method for nonlinear dimensionality reduction in batch mode, but need to run its entirety inefficiently if the data comes sequentially. In this paper, we present an extension of ISOMap, namely I-ISOMap, augmenting the existing ISOMap framework to the situation where additional points become available after initial manifold is constructed. The MDS step, as a key component in ISOMap, is adapted by introducing Spring model and sampling strategy. As a result, it consumes only linear time to obtain a stable layout due to the Spring model's iterative nature. The proposed method outperforms earlier work by Law [1], where their MDS step runs within quadratic time. Experimental results show that I-ISOMap is a precise and efficient technique for capturing evolving manifold.