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Computation 2019, 7(1), 15; https://doi.org/10.3390/computation7010015

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

1
Department of Artificial Intelligence, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Tehran 16788-15811, Iran
2
Faculty of Computer Engineering, University of the Basque Country, 20018 San Sebastian, Spain
3
Ikerbasque, Foundation for science, 48013 Bilbao, Spain
*
Author to whom correspondence should be addressed.
Received: 30 January 2019 / Revised: 22 February 2019 / Accepted: 25 February 2019 / Published: 7 March 2019
(This article belongs to the Section Computational Engineering)
Full-Text   |   PDF [2899 KB, uploaded 7 March 2019]   |  

Abstract

In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms. View Full-Text
Keywords: manifold learning; graph construction; multi-metric fusion; face recognition manifold learning; graph construction; multi-metric fusion; face recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bahrami, S.; Bosaghzadeh, A.; Dornaika, F. Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning. Computation 2019, 7, 15.

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