Multidimensional Scaling

Also known as MDS

Latest papers

11 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Gromov-Wasserstein Distillation for Inductive Multi-View Embedding
Sep 30, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteRepresentation Learning Multi-View Learning
Instituto Federal do Ceará (IFCE) Canindé, Ceará, Brazil · Federal University of Ceará (UFC) Fortaleza, Ceará, Brazil · Sigma Nova Paris, France
Robust conditional dimension reduction for dissimilarity data
Sep 5, 2026Xiao Ling, Anh BuiMultidimensional Scaling Dimensionality Reduction
Department of Mathematics, Auburn University at Montgomery Montgomery, AL 36117, USA · Department of Mathematics and Statistics, Virginia Commonwealth University Richmond, VA 23284, USA
Adjusted Wasserstein distances for bridging empirical and true distributions with applications to MDS
Jun 29, 2026Flor Martinez-Sermeno, Arturo Jaramillo, Johan Van HorebeekMultidimensional Scaling Dimensionality Reduction
Optimizing Multidimensional Scaling in Gini Metric Spaces
May 24, 2026Cassandra Mussard, Stéphane MussardMultidimensional Scaling Dimensionality Reduction
CESBIO, Univ. Toulouse Toulouse, France · CHROME, Univ. Nˆımes UM6P, AIRESS Nˆımes, France
Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
May 1, 2026Daniel Hangan, Stephen Kobourov, Jacob MillerScientific Visualization Data Visualization
Technical University of Munich
Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
Apr 26, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteRepresentation Learning Multidimensional Scaling
Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Canindé, Ceará, Brazil · Federal University of Ceara, Fortaleza, Ceará, Brazil · Sigma Nova, Paris, France
High-dimensional Semi-supervised Classification via the Fermat Distance
Apr 26, 2026Ruoxu Tan, Yiming ZangMultidimensional Scaling k-Nearest Neighbors
School of Mathematical Sciences and School of Economics and Management, Tongji University · Department of Sciences, North China University of Technology
On the Geometry of Positional Encodings in Transformers
Apr 6, 2026Giansalvo CirrincioneMultidimensional Scaling Positional Encoding
Laboratoire LTI, Université de Picardie Jules Verne Chemin du Thil, 80025 Amiens, France
Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
Apr 3, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteMultidimensional Scaling Multi-View Learning
Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Canindé, Ceará, Brazil · Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil · Sigma Nova, Paris, France
Manifold-Matching Autoencoders
Mar 17, 2026Laurent Cheret, Vincent Létourneau, Isar Nejadgholi +3Multidimensional Scaling Autoencoders
Department of Computer Science, University of Ottawa, Ottawa, Canada · MILA, Montréal, Canada · National Research Council of Canada, Ottawa, Canada
Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction
May 10, 2025Michael W. Trosset, Kaiyi Tan, Minh Tang +1Multidimensional Scaling Kernel Methods
Department of Statistics, Indiana University. · Department of Statistics, North Carolina State University. · Department of Applied Mathematics & Statistics, Johns Hopkins University.

Sep 30, 2026J/K move · Enter open · S save

Gromov-Wasserstein Distillation for Inductive Multi-View Embedding

Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante

Instituto Federal do Ceará (IFCE) Canindé, Ceará, Brazil · Federal University of Ceará (UFC) Fortaleza, Ceará, Brazil · Sigma Nova Paris, France

Representation Learning Multi-View Learning Manifold Learning Relational Knowledge Distillation Multidimensional Scaling

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Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing no explicit mapping for unseen samples. We introduce an inductive framework based on barycentric distillation. A GW-MDS teacher learns a latent support and an optimal transport plan from the training data, and barycentric projection converts the resulting coupling into sample-aligned targets. A neural student then learns an explicit out-of-sample mapping, avoiding additional relational-matrix construction and GW optimization at inference. We formulate the approach for single-view data and extend it to Mean-GWMDS and Multi-GWMDS teachers through consensus and selected-projection targets learned by a multi-view student with view-specific encoders. We also investigate a direct neural baseline trained solely with a GW objective. Experiments on synthetic and real-world data using Euclidean, geodesic, and cosine relations show that the distilled models preserve the teacher geometry on unseen samples and consistently outperform direct neural GW training in sample-indexed relational preservation. These results establish barycentric projection as an effective bridge between transductive GW embeddings and inductive neural mappings.