Neighborhood preserving embedding

By Xiaofei He, Deng Cai, Shuicheng Yan, HongJiang Zhang. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005

1,708 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ff9efce2c082d9e164065b8d6455e8c8a7233a26

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Citations in the corpus

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “[73] proposed a semisupervised local discriminant (SELD) analysis to overcome this problem by combining unsupervised methods (LLFE [23]–[25]) and a supervised method (LDA [37]) in a novel framework without any free parameters.”
On Similarity Preserving Feature Selection (2013, methodology): “…algorithms in this category include: Multidimensional Scaling (MDS) [6], ISOMAP [38], Locally Linear Embedding (LLE) [31], Laplacian Eigenmaps [4], Semidefinite Embedding (SDE) [41], Neighborhood Preserving Embedding (NPE) [20], and Structure Preserving Embedding (SPE) [34], to name a few.”
Latent Low-Rank Representation for subspace segmentation and feature extraction (2011, methodology): “For comparison, we also list the results of some popular dimension reduction methods, including Principal Component Analysis (PCA), Locality Preserving Projection (LPP) [9], Neighborhood Preserving Embedding (NPE) [10] and Nonnegative Matrix Factorization (NMF) [8].”
Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “APPEARANCE-BASED methods have been widely used in face recognition, and a large number of such algorithms have been proposed in recent years [2], [3], [4], [5], [9], [14], [15], [16], [23], [24], [25], [ 41], [46], [49], [51], [53], [55].”
Robust Sparse Linear Discriminant Analysis (2019, background): “Locality preserving projection (LPP) [14], sparsity preserving projections (SPP) [15], and neighborhood preserving embedding (NPE) [16] are also the popular feature extraction methods which learn their projections from different geometric structures of original data.”
Dimensionality Reduction: A Comparative Review (2008, background): “The popularity of LLE has led to the proposal of linear variants of the algorithm [49,65], and to successful applications to, e.”
Joint embeddings of shapes and images via CNN image purification (2015, methodology): “Aside from the aforementioned PCA and MDS with Sammon mapping methods, we also evaluated the embedding space obtained by applying Local Linear Embedding (LLE) [Roweis and Saul 2000] and Neighborhood Preserving Embedding (NPE) [He et al. 2005] on F.”
Spectral Regression for Efficient Regularized Subspace Learning (2007, methodology): “Many recently proposed popular linear subspace learning algorithms, e.g., LDA [1], LPP [13], and NPE [12] can be interpreted as the linear extensions of specific graph embedding.”
Linear dimensionality reduction: survey, insights, and generalizations (2014, methodology): “Related to LPP, neighborhood preserving embedding (NPE) (He et al., 2005) has a largely parallel motivation.”
Learning With $\ell ^{1}$-Graph for Image Analysis (2010, methodology): “In this section, we briefly introduce how to benefit from -graph for these tasks.”
Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015, background): “In contrast, bigger d do not lead to better performance, while LPP or NPE is applied to learn patch manifold.”
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015, methodology): “In LPP, we set the k-nearest-neighbors parameter as k¼10 and the kernel parameter t¼1, while in NPE, we also set k¼10.”
Learning a Spatially Smooth Subspace for Face Recognition (2007, methodology): “NPE is the linear extension of graph embedding problem GE( W NPE , I ).”
Embedding with Autoencoder Regularization (2013, background): “Recently there have been lots of nonlinear approaches to compute a low-dimensional embedding, including Isomap [24], LLE [19], Laplacian eigenmaps [1], marginbased embedding algorithms [7] and their variants [17,8].”
Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction (2014, background): “Neighborhood Preserving Embedding (NPE) [3] and Locality Preserving Projection (LPP) [4] are linear approximations to LLE and LE to handle the out-of-sample problem, respectively.”
Generalized Multiview Analysis: A discriminative latent space (2012, methodology): “Plugging in different (A,B) pairs for different feature extraction techniques in our framework we can obtain multi-view extensions of PCA [23], LDA [1], LPP [10], NPE [9] and MFA [25].”
Structure-Preserving Binary Representations for RGB-D Action Recognition (2016, background): “Neighborhood Pre-serving Embedding (NPE) [51] also tries to preserve the local representation of data.”