Ranking on Data Manifolds

By Dengyong Zhou, Jason Aaron Edward Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf. Neural Information Processing Systems, 2003

797 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/429eb35f06a90892a9595545d18e46ddfe981152

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Citations in the corpus

Saliency Detection via Graph-Based Manifold Ranking (2013, methodology): “In [39], a ranking method that exploits the intrinsic manifold structure of data (such as image) for graph labelling is proposed.”
Unsupervised Large Graph Embedding (2017, background): “hashing (Li, Hu, and Nie 2017), clustering (Ng, Jordan, and Weiss 2001), semi-supervised learning (Zhou et al. 2003; Zhu 2008), dimensionality reduction (Nie et al.”
Unsupervised Detection of Regions of Interest Using Iterative Link Analysis (2009, methodology): “For the density function, we use the PageRank vector because it is proportional to the vertex degree if the graph is symmetric and connected [25].”
Learning Context-Sensitive Shape Similarity by Graph Transduction (2010, methodology): “[26] modified the LGC for the information retrieval.”
Scalable Person Re-identification on Supervised Smoothed Manifold (2017, background): “To overcome this issue, potential solutions can be semisupervised [68, 66] or unsupervised [67, 5, 58, 12, 6] algorithms about manifold learning.”
Mining on Manifolds: Metric Learning Without Labels (2018, methodology): “1 Given se, we employ a random walk on the Euclidean nearest neighbor graph G to measure the manifold similarity sm [69].”
A Graphbased Framework for Multi-Task Multi-View Learning (2011, methodology): “Following the convention in (Zhou et al., 2003), we set μi = 0.”
Nonlinear Dimensionality Reduction with Local Spline Embedding (2009, background): “The utility of manifold learning has been demonstrated in many applications such as visualization [6], [50], [20], image analysis [11], image superresolution [8], denoising [25], motion analysis [26], data ranking [62], semisupervised learning [52], and so on.”
Disentangled Inference for GANs With Latently Invertible Autoencoder (2019, methodology): “ variations, we use the three categories from the large-scale LSUN database (Yu et al., 2015), i.e. cat, car, and bedroom. For each category, the 0.1 million images are selected by ranking algorithm (Zhou et al., 2003) from the ﬁrst 0.5 million images in the dataset. Each cat and bedroom images are ”
On combining machine learning with decision making (2011, methodology): “In machine learning, our work bears a slight resemblance to work on graph-based regularization (Agarwal 2006; Belkin et al. 2006; Zhou et al. 2004), but their goal is to obtain probability estimates that are smoothed on a graph with suitably designed edge weights.”
Image retrieval: Ideas, influences, and trends of the new age (2008, methodology): “This idea is explored and applied to image similarity and ranking in [He 2004; Vasconcelos and Lippman 2005; He et al. 2004; He et al. 2004a; Zhou et al. 2003].”
Identifying remote protein homologs by network propagation (2005, background): “More recently, in [31], the convergence of a similar algorithm to (1) is shown, and a closed form expression is given.”
Learning with Partially Absorbing Random Walks (2012, methodology): “The regularization parameter in Manifold Ranking is set to 0.99, following [18].”
Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (2016, methodology): “As for the ranking procedure, the manifold structure of data is considered because it can explore the intrinsic data nature [40], [44].”