5 papers
Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction
Michael W. Trosset, Kaiyi Tan, Minh Tang +1
The problem of using proximity (similarity or dissimilarity) data for the purpose of "adding a point to a vector diagram" was first studied by J.C. Gower in 1968. Since then, a num…
Consistent estimation of generative model representations in the data kernel perspective space
Aranyak Acharyya, Michael W. Trosset, Carey E. Priebe +1
Generative models, such as large language models and text-to-image diffusion models, produce relevant information when presented a query. Different models may produce different inf…
Convergence guarantees for response prediction for latent structure network time series
Aranyak Acharyya, Francesco Sanna Passino, Michael W. Trosset +1
In this article, we propose a technique to predict the response associated with an unlabeled time series of networks in a semisupervised setting. Our model involves a collection of…
Continuous Multidimensional Scaling
Michael W. Trosset, Carey E. Priebe
Multidimensional scaling (MDS) is the act of embedding proximity information about a set of objects in -dimensional Euclidean space. As originally conceived by the psychomet…
Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings
Konstantinos Pantazis, Michael Trosset, William N. Frost +2
Theoretical and empirical evidence suggests that joint graph embedding algorithms induce correlation across the networks in the embedding space. In the Omnibus joint graph embeddin…