Data Fusion and Multicue Data Matching by Diffusion Maps

By Stéphane Lafon, Yosi Keller, Ronald R. Coifman. 2006

341 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Fast Learning from Distributed Datasets without Entity Matching (2016, methodology): “On the other hand, learning-based ER [Bilenko and Mooney, 2003] as well as manifold alignment techniques [Lafon et al., 2006] are viable only knowing some ground truth matches — which are not required for working with ra-”
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion (2015, methodology): “In the LEAP framework, similarly to the work by Liu et al. [15], and by Lafon et al. [16], a low dimensional representation of the data is usedtoﬁndasurrogatemeasurementofthemorphologicallysim-ilarity between datasets.”
A spectral notion of Gromov–Wasserstein distance and related methods (2011, methodology): “A second, third and fourth sets of previously proposed ideas that we address with our construction and relate to are those of [20,27,28] and [24].”
A Survey of Multi-View Representation Learning (2016, neutral): “[291] propose a two-step manifold alignment to solve this problem.”
Anisotropic diffusion on sub-manifolds with application to Earth structure classification (2012, background): “Extension of functions on the data can be done in various ways (see for example [9–11]).”
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, neutral): “Recent investigations that seek to jointly exploit the global and local characteristics of images [28], [29] and manifold alignment [30], [31] provide the foundation for a correspondence-based framework to classify hyperspectral data acquired in multiple time periods [32] or from spatially disjoint ”
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology (2017, background): “for integrating heterogeneous descriptions of the same set of genes (Lafon et al., 2006), and fused data is more informative (Blanchet and Smolinska, 2016).”
Manifold alignment using Procrustes analysis (2008, neutral): “The work of Lafon et al. (Lafon et al., 2006) is based on a similar framework as ours.”
Generalized Tree-Based Wavelet Transform (2010, methodology): “We note that when the dataset is a weighted graph, but no feature points are available, the diffusion map framework [17], [18] can be employed in order to obtain such points from the graph.”