NDLI: Online incremental clustering with distance metric learning for high dimensional data

Content Provider	IEEE Xplore Digital Library
Author	Okada, S. Nishida, T.
Copyright Year	2011
Description	Author affiliation: Dept.of Computational Intelligence and Systems Science, Tokyo Institute of Technology of JAPAN (Okada, S.) \|\| Dept.of Intelligence Science and Technology, The Kyoto University of JAPAN (Nishida, T.)
Abstract	In this paper, we present a novel incremental clustering algorithm which assigns of a set of observations into clusters and learns the distance metric iteratively in an incremental manner. The proposed algorithm SOINN-AML is composed based on the Self-organizing Incremental Neural Network (Shen et al 2006), which represents the distribution of unlabeled data and reports a reasonable number of clusters. SOINN adopts a competitive Hebbian rule for each input signal, and distance between nodes is measured using the Euclidean distance. Such algorithms rely on the distance metric for the input data patterns. Distance Metric Learning (DML) learns a distance metric for the high dimensional input space of data that preserves the distance relation among the training data. DML is not performed for input space of data in SOINN based approaches. SOINN-AML learns input space of data by using the Adaptive Distance Metric Learning (AML) algorithm which is one of the DML algorithms. It improves the incremental clustering performance of the SOINN algorithm by optimizing the distance metric in the case that input data space is high dimensional. In experimental results, we evaluate the performance by using two artificial datasets, seven real datasets from the UCI dataset and three real image datasets. We have found that the proposed algorithm outperforms conventional algorithms including SOINN (Shen et al 2006) and Enhanced SOINN (Shen et al 2007). The improvement of clustering accuracy (NMI) is between 0.03 and 0.13 compared to state of the art SOINN based approaches.
Starting Page	2047
Ending Page	2054
File Size	1981965
Page Count	8
File Format	PDF
ISBN	9781424496358
ISSN	21614407
e-ISBN	9781424496372
DOI	10.1109/IJCNN.2011.6033478
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Measurement Equations Mathematical model Algorithm design and analysis Feature extraction Joining processes
Content Type	Text
Resource Type	Article

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