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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2018

08.02.2018

Centroid Neural Network with Pairwise Constraints for Semi-supervised Learning

verfasst von: Minh Tran Ngoc, Dong-Chul Park

Erschienen in: Neural Processing Letters | Ausgabe 3/2018

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Abstract

A clustering algorithm for datasets with pairwise constraints using the Centroid Neural Network (Cent.NN) is proposed in this paper. The proposed algorithm, referred to as the Centroid Neural Network with Pairwise Constraints (Cent. NN-PC) algorithm, utilizes Cent.NN as its backbone algorithm for data clustering and adopts a semi-supervised learning process for pairwise constraints. A newly formulated energy function is adopted from the original Cent.NN algorithm for the proposed Cent.NN-PC algorithm, introducing penalty terms for violating constraints. The weight update procedure of the proposed Cent.NN-PC algorithm finds optimal prototypes for the given dataset that minimize the quantization error while minimizing the number of violated constraints. In order to evaluate the performance of the proposed Cent.NN-PC algorithm, experiments on six different datasets from the UCI database and two bioinformatics datasets from the KEEL repository are carried out. The performance of the proposed algorithm is compared to that of the the Linear Constrained Vector Quantization Error (LCVQE) algorithm, one of the most commonly used algorithms for data clustering with pairwise constraints. In the experiments, five different numbers of pairwise constraints are utilized to evaluate the clustering performance with constraints of different sizes. The results show that the proposed Cent.NN-PC algorithm outperforms the LCVQE algorithm on most performance criteria, including the total quantization error, the number of violated constraints, and on the three performance metrics of the classification accuracy rate, F-score, and NMI measure outcome. The experiments also show that Cent.NN-PC provides much more stable clustering results at an improved operational speed compared to LCVQE.
Vorheriger Artikel Structure Preserving Sparse Coding for Data Representation
Nächster Artikel New Algebraic Criteria for Global Exponential Periodicity and Stability of Memristive Neural Networks with Variable Delays

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Metadaten
Titel
Centroid Neural Network with Pairwise Constraints for Semi-supervised Learning
verfasst von
Minh Tran Ngoc
Dong-Chul Park
Publikationsdatum
08.02.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9794-8

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