Abstract
In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving graph embedding (DSPGE). Unlike many existing graph embedding methods such as locality preserving projections (LPP) and sparsity preserving projections (SPP), the aim of DSPGE is to preserve the sparse reconstructive relationships of data while simultaneously capture the geometric and discriminant structure of data in the embedding space. Through the sparse reconstruction and class-specific adjacent graphs, DSPGE characterizes the intra-class and inter-class sparsity preserving scatters, seeking to achieve the optimal projections that simultaneously maximize the inter-class sparsity preserving scatter and minimize intra-class sparsity preserving scatter. The effectiveness of the proposed DSPGE is demonstrated on two popular face databases, compared to up-to-date methods. The experimental results show that DSPGE outperforms the competing methods with the satisfactory classification performance.
Original language | English |
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Title of host publication | CEC 2016 |
Subtitle of host publication | 2016 IEEE Congress on Evolutionary Computation |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 4250-4257 |
Number of pages | 8 |
ISBN (Electronic) | 9781509006229, 9781509006243 |
ISBN (Print) | 9781509006236 |
DOIs | |
Publication status | Published - 14 Nov 2016 |
Externally published | Yes |
Event | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 |
Other
Other | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
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Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |
Keywords
- dimensionality reduction
- graph embedding
- sparse representation,
- face recognition