Two-dimensional locality preserving projection based on Maximum Scatter Difference ⋆
نویسندگان
چکیده
In this paper, we propose a Two-dimensional locality preserving projection based on maximum scatter difference (2D-DLPP/MSD). 2D-LPP/MSD use additive principle to preserve the locality by maximizing the between-class scatter and within-class scatter instead of using multiplicative principle of 2D-DLPP. Theoretically, we also discuss the influence of balance factor α on performance and reveal the relations between 2D-LPP/MSD and 2D-DLPP. Experimental results on the ORL and Yale face databases show the effectiveness of the proposed 2D-DLPP/MSD.
منابع مشابه
Kernel Scatter-difference Based Discriminant Locality Preserving Projection for Image Recognition ?
Locality preserving projection (LPP) aims at finding an embedded subspace that preserves the local structure of data. Though LPP can provide intrinsic compact representation for image data, it has limitations on image recognition. In this paper, an improved algorithm called kernel scatter-difference based discriminant locality preserving projection (KSDLPP) is proposed. KSDLPP uses kernel trick...
متن کاملDiscriminant Locality Preserving Projection
In this study, we proposed an improved LPP method named Scatter-Difference Discriminant Locality Preserving Projection (SDDLPP). It considers discriminant information by maximizing the scatter-difference, which makes it have better classification capability. SDDLPP also avoids the singularity problem for the highdimensional data matrix and can be directly applied to the small sample size proble...
متن کاملScatter-difference discriminant locality preserving projections for palmprint recognition
We develop an improved LPP method called scatter-difference discriminant locality preserving projections (SDLPP) for palmprint recognition. SDLPP has better classification capability which benefits from discriminant information obtained by maximizing the difference of between-class separability and within-class similarity. SDLPP avoids the singularity problem for the high-dimensional data matri...
متن کاملFisher Locality Preserving Projections for Face Recognition ?
In this paper, a novel dimensionality reduction method termed Fisher Locality Preserving Projections (FLPP) is proposed by introducing the maximum scatter difference criterion (MSDC) to the objective function of Locality Preserving Projections (LPP). FLPP not only inherits the advantages of LPP which attempts to preserve the local structure, but also makes full use of class information and orth...
متن کاملLocal Graph Embedding Based on Maximum Margin Criterion (LGE/MMC) for Face Recognition
Locally linear embedding (LLE) is an efficient dimensional reduction algorithm for nonlinear data, and the low dimensional data can maintain topological relations in the original space after the processing. But this algorithm main application is not very good in the data dimensional reduction, the visualization and learning effects of data classification question and so on. In ordered to solve ...
متن کامل