Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3189–3207 | Cite as

Discriminative unsupervised 2D dimensionality reduction with graph embedding



Dimensionality reduction is a great challenge in high dimensional unlabelled data processing. The existing dimensionality reduction methods are prone to employing similarity matrix and spectral clustering algorithm. However, the noises in original data always make the similarity matrix unreliable and degrade the clustering performance. Besides, existing spectral clustering methods just focus on the local structures and ignore the global discriminative information, which may lead to overfitting in some cases. To address these issues, a novel unsupervised 2-dimensional dimensionality reduction method is proposed in this paper, which incorporates the similarity matrix learning and global discriminant information into the procedure of dimensionality reduction. Particularly, the number of the connected components in the learned similarity matrix is equal to cluster number. We compare the proposed method with several 2-dimensional unsupervised dimensionality reduction methods and evaluate the clustering performance by K-means on several benchmark data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.


Similarity matrix Spectral clustering Global discriminant information 



This work was jointly supported by Natural Science Basic Research Plan in Shannxi Province of China No. 2017JM6056, and Designing inter-core Datapath for voltage frequency island mpSoC No. 15JK1726.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina
  2. 2.Faculty of Electrical Engineering and InformaticsBudapest University of Technology and EconomicsBudapestHungary

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