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Collaborative Representation Based Neighborhood Preserving Projection for Dimensionality Reduction
- Source :
- Communications in Computer and Information Science ISBN: 9789811072987, CCCV (1)
- Publication Year :
- 2017
- Publisher :
- Springer Singapore, 2017.
-
Abstract
- Collaborative graph-based discriminant analysis (CGDA) has been recently proposed for dimensionality reduction and classification. It uses available samples to construct sample collaboration via L2 norm minimization-based representation, thus showing great computational efficiency. However, CGDA only constructs the intra-class graph, so it only takes into account local geometry and ignores the separability for samples in different classes. In this paper, we propose a novel method termed as collaborative representation based neighborhood preserving projection (CRNPP) for dimensionality reduction. By incorporating the intra-class and inter-class discriminant information into the graph construction of collaborative representation coefficients, CRNPP not only maintains the same level of time cost as CGDA, but also preserves both global and local geometry of the data simultaneously. In this way, the collaborative relationship of the data from the same class is strengthened while the collaborative relationship of the data from different classes is inhibited in the projection subspace. Experiments on benchmark face databases validate the effectiveness and efficiency of the proposed method.
- Subjects :
- 021103 operations research
Theoretical computer science
Computer science
Dimensionality reduction
0211 other engineering and technologies
02 engineering and technology
Linear discriminant analysis
01 natural sciences
Facial recognition system
Time cost
010104 statistics & probability
Discriminant
Graph (abstract data type)
Minification
0101 mathematics
Subspace topology
Subjects
Details
- ISBN :
- 978-981-10-7298-7
- ISBNs :
- 9789811072987
- Database :
- OpenAIRE
- Journal :
- Communications in Computer and Information Science ISBN: 9789811072987, CCCV (1)
- Accession number :
- edsair.doi...........4ef34fd1714058d1f9044fddb13cada1