Structure preserving embedding

By Blake Shaw, Tony Jebara. 2009

236 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistical and Nonlinear Physics

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Citations in the corpus

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, neutral): “SPE [105] KNN,W 1⁄4 argmaxk 0 trðWÂÞ, s.”
Learning Two-Branch Neural Networks for Image-Text Matching Tasks (2017, background): “Such within-view neighborhood preservation constraints have been extensively explored in the metric learning literature [38], [39], [40], [41], [42], [43].”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, background): “Structure Preserving Embedding (SPE) Structure Preserving Embedding ([33]) is another approach which extends Laplacian Eigenmaps.”
Laplacian Auto-Encoders: An explicit learning of nonlinear data manifold (2015, methodology): “Among them, local methods including LLE [3], Hessian LLE [4], and Laplacian Eigenmap [5] exploit and preserve local neighborhood structures of training points, and global methods, such as Isomap [6] among others [7,8], try to preserve global topological structures of all the data points.”
Label Informed Attributed Network Embedding (2017, background): “Shaw and Jebara [30] exploited a semideﬁnite program to learn a low-dimensional representation that well preserves the global topological structure.”
Learning Deep Structure-Preserving Image-Text Embeddings (2015, background): “Such within-view structure preservation constraints have been extensively explored in the metric learning literature [19, 32, 40, 41, 48, 53].”
Piecewise Flat Embedding for Image Segmentation (2018, background): “serving embedding [48], attempt to preserve local and global”
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.”
Heterogeneous Network Embedding via Deep Architectures (2015, background): “[38] proposed a structure preserving embedding framwork that embeds graphs to a low-dimensional Euclidean space with global topological properties preserved.”
Dimensionality Reduction: A Comparative Review (2008, methodology): “These variants include factor analysis [117], Gaussian Process Latent Variable Models [80], principal curves [28], kernel maps [118], conformal eigenmaps [113], Geodesic Nullspace Analysis [24], Structure Preserving Embedding [114], variants of multidimensional scaling [3, 38, 45, 62, 92], technique”
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (2015, background): “Geometric embeddings oﬀer a powerful and ﬂexible approach for learning on graph structured data by capturing the structure of the graph and exploiting the geometry of Euclidean spaces to devise fast algorithms [34, 23].”
Structural Deep Network Embedding (2016, background): “However, the underlying structure of the network is very complex [24].”
Signed Laplacian Embedding for Supervised Dimension Reduction (2014, background): “…Preserving Projections (LPP) (He and Niyogi 2004), Local Tangent Space Alignment (LTSA) (Zhang and Zha 2005), Structure Preserving Em-bedding (SPE) (Shaw and Jebara 2009), Maximum Variance Correction (MVC) (Chen, Chen, and Weinberger 2013), and Multi-scale Manifold Learning (Wang and Mahadevan…”
Probabilistic Latent Document Network Embedding (2014, methodology): “The objective of experiments is to validate the effectiveness of our topic-based embedding method PLANE.”