Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

By Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zhang, Qiang Yang, Stephen Lin. 2007

2,974 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, Graph Learning, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Representation & Self-Supervised Learning

In Graph Learning

In Signal Processing

Citations in the corpus

node2vec: Scalable Feature Learning for Networks (2016, methodology): “…and non-linear dimensionality reduction techniques such as Principal Component Analysis, Multi-Dimensional Scaling and their extensions [3, 27, 30, 35] optimize an objective that transforms a representative data matrix of the network such that it maximizes the variance of the data representation.”
LINE: Large-scale Information Network Embedding (2015, methodology): “, the K-nearest neighbor graph of data, and then embed the affinity graph [22] into a low dimensional space.”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “[35] survey a list of such methods including Principal Component Analysis (PCA) [36], Linear Discrimant Analysis (LDA) [37], ISOMAP [38], Multidimesional Scaling (MDS) [39], Locality Preserving Properties (LPP) [40] and Kernel Eigenmaps [41].”
Coupled Kernel Embedding for Low-Resolution Face Image Recognition (2012, background): “Compared with highresolution (HR) images, these LR images lose some discriminative details across different subjects.”
Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection (2016, methodology): “[33] proposed a supervised dimensionality reduction algorithm called Marginal Fisher Analysis (MFA), where the intrinsic graph characterizes the intra-class compactness and connects each data point with its neighboring points of the same class, while the penalty graph characterizes the inter-class s”
Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “We solve this problem in an iterative manner as inspired by recent advances in high order tensor decomposition [16, 33].”
Multiview Consensus Graph Clustering (2019, methodology): “Since most multiview learning methods need to exploit the similarity, k-means clustering [23], non-negative matrix factorization (NMF) [24], SC [3], graph embedding [25], subspace learning [26], and multiple-kernel learning [27] are usually extended to multiview setting.”
Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (positive): “This rule plays a critical role in developing various kinds of algorithms including dimensionality reduction algorithms [45], clustering algorithms [5] and semi-supervised learning algorithms [54].”
A survey of multilinear subspace learning for tensor data (2011, methodology): “3, there have been some developments already in this area [43, 48, 84–86].”
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification (2016, methodology): “Different from traditional graph embedding methods [31], such as multi-dimensional scaling [8], IsoMap [28], LLE [18], Laplacian Eigenmap [2], the network embeddings are more scalable and shown better performance [17, 26].”
metapath2vec: Scalable Representation Learning for Heterogeneous Networks (2017, background): “5 RELATEDWORK Network representation learning can be traced back to the usage of latent factor models for network analysis and graph mining tasks [10, 34], such as the application of factorization models for recommendation systems [14, 16], node classication [32], relational mining [19], and role d”
Heterogeneous Information Network Embedding for Recommendation (2017, methodology): “On the other hand, network embedding has shown its potential in structure feature extraction and has been successfully applied in many data mining tasks [39], [40], [41], [42], such as classification [43], clustering [44], [45] and recommendation [46], [47].”
Generalized Multiview Analysis: A discriminative latent space (2012, methodology): “Methods that fit this equation include PCA [23, 25], LDA [1, 25], LPP [10, 25], CCA, and MFA [25].”