Manifold matching: Joint optimization of fidelity and commensurability

Priebe, Carey E.; Marchette, David J.; Ma, Zhiliang; Adali, Sancar

doi:10.1214/12-bjps188

Cited by 18 publications

(29 citation statements)

References 17 publications

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“…In Ref 1 the matching hypothesis test approach described above was studied for these data, and the joint embedding approach was shown to have higher power for this test than either the separate embedding or the canonical correlation method. In Ref 10 the data were used for a classification task.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, given a new document in one language, we embed the document into our joint space and match it with the nearest article in the other language. Many of the ideas discussed herein can be found in more detail in Ref 1. We give the basic ideas, with a few new ideas for consideration, then provide some experimental results on a set of 1382 Wikipedia article pairs in English and French.…”

Section: Introductionmentioning

confidence: 99%

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Implicit translation

Marchette

2011

WIREs Computational Stats

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This article describes a joint embedding approach to matching documents in multiple languages. The approach is general, operating on dissimilarity matrices, and is thus applicable to any problem of fusing disparate information. We apply it to a problem of implicit translation, in which documents in two different languages are matched. WIREs Comp Stat 2012, 4:28–34. doi: 10.1002/wics.181 This article is categorized under: Data: Types and Structure > Text Data Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implicit translation

Marchette

2011

WIREs Computational Stats

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“…Next we apply SRC to vertex classification on real-world networks. The first graph is collected from Wikipedia article hyperlinks (Priebe et al, 2013). A total of 1382 English documents based on the 2-neighborhood of the English article "algebraic geometry" are collected, and the adjacency matrix is formed via the documents' hyperlinks.…”

Section: Network Connectivitymentioning

confidence: 99%

Sparse Representation Classification via Screening for Graphs

Shen,

Chen,

Dong

et al. 2019

Preprint

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The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency for random graphs drawn from stochastic blockmodels. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance but significantly faster.

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“…Our joint vertex classification consists of two main steps: first, a fusion information technique, namely the omnibus embedding methodology by Priebe et al 27 ; and secondly, the inferential task of vertex classification.…”

Section: Joint Vertex Classificationmentioning

confidence: 99%

A joint graph inference case study: theC. eleganschemical and electrical connectomes

Chen

Vogelstein

Lyzinski

et al. 2016

Worm

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We investigate joint graph inference for the chemical and electrical connectomes of the Caenorhabditis elegans roundworm. The C. elegans connectomes consist of 253 non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from the perspectives of seeded graph matching and joint vertex classification. Our results suggest that connectomic inference should proceed in the joint space of the two connectomes, which has significant neuroscientific implications.

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Manifold matching: Joint optimization of fidelity and commensurability

Cited by 18 publications

References 17 publications

Implicit translation

Implicit translation

Sparse Representation Classification via Screening for Graphs

A joint graph inference case study: theC. eleganschemical and electrical connectomes

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