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Found 7 papers, 2 papers with code
Continuous Multidimensional Scaling
Multidimensional scaling (MDS) is the act of embedding proximity information about a set of $n$ objects in $d$-dimensional Euclidean space.
Semisupervised regression in latent structure networks on unknown manifolds
We assume that the latent position vectors lie on an unknown one-dimensional curve and are coupled with a response covariate via a regression model.
Popularity Adjusted Block Models are Generalized Random Dot Product Graphs
We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special case of the GRDPG in which communities correspond to mutually orthogonal subspaces of latent vectors.
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure
Manifold learning techniques for nonlinear dimension reduction assume that high-dimensional feature vectors lie on a low-dimensional manifold, then attempt to exploit manifold structure to obtain useful low-dimensional Euclidean representations of the data.
Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs
We submit that techniques for manifold learning can be used to learn the unknown submanifold well enough to realize benefit from restricted inference.
Nonparametric semi-supervised learning of class proportions
This problem can be decomposed into two steps: (i) the development of accurate predictors that discriminate between positive and unlabeled data, and (ii) the accurate estimation of the prior probabilities of positive and negative examples.
Fast Embedding for JOFC Using the Raw Stress Criterion
The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the same set of objects.
