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locality preserving projection.
This paper proposes a two-dimensional regularized locality preserving projection (2DRLPP) algorithm for feature extraction, which combines locality preserving projection (LPP) method with data roughness regularization.
The algorithm, which is designed based on the locality preserving projection, accepts tensors as inputs.
Recently, the manifold learning algorithm-local preserving projection (LPP) is proposed [7].
In our experiment, we use the linear discriminant analysis (LDA) approach and locality preserving projection (LPP) approach for intersession compensation.
Locality preserving projection (LPP) [10, 11] is different from LDA which effectively preserves global structure and linear manifold.
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Since this high-dimensional feature space is too large to perform fast and robust face recognition in practice, dimensionality reduction techniques like principal component analysis (PCA) [21], linear discriminant analysis (LDA) [22], or locality preserving projections (LPP) [23] can be used to project the vectorized face images into a smaller dimensional subspace.
Dimensionality reduction is performed by locality preserving projections.
The SDMP was developed based on sparsity preserving projections, and sparse manifold clustering and embedding.
Locality preserving projections (LPPs) have been widely used for extracting compact and discriminative information from such high-dimensional data.
L. Zhu et al. [27] propose the orthogonal discriminant locality preserving projections (ODLPP) by orthogonalizing the basis vectors.
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