arXiv Machine Learning August 19, 2026 By Shirin Mohebi, Guillaume Bied, Jefrey Lijffijt

How smoothing the affinity matrix affects neighborhood preservation in t-SNE

Read the original on arXiv Machine Learning →

arXiv:2608. 17190v1 Announce Type: new Abstract: Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local neighborhood preservation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

Jul 2

FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling

arXiv:2605. 11428v2 Announce Type: replace Abstract: Exploratory analysis of high-dimensional data rarely stops at a single embedding.

By Hongmin Li

rag benchmarks

arXiv Machine Learning

Jun 3

A Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering

arXiv:2606. 02887v1 Announce Type: new Abstract: Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as $WW^T$ with nonnegative rectangular factor $W$.

By Ryan Swart, Johannes Brust

benchmarks

arXiv Machine Learning

2d ago

High-dimensional nonparametric changepoint detection via low-rank degree-two density projection

arXiv:2608. 13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified.

By Guoqing Zhang, Zhaixin Chen

efficiency benchmarks

arXiv Machine Learning

Jul 28

Low-Rank Dependence Decomposition via Accelerated Symmetric Non-negative Matrix Factorization

arXiv:2607. 24518v1 Announce Type: new Abstract: Symmetric non-negative matrix factorization (SymNMF) recovers latent group structure from a dependence matrix, but its dense, quadratic-memory objective has confined prior work to moderate sizes.

By Lavinia Ghita, Dhruv Desai, Jake Goldberg, Roman Yokunda Enzmann

benchmarks

arXiv AI

1d ago

Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning

arXiv:2608. 15313v1 Announce Type: cross Abstract: In this paper, we propose SHOPCA (Shape Operator-based Principal Component Analysis), a novel method for unsupervised metric learning and dimensionality reduction that incorporates differential geometric information into the covariance structure of classical PCA.

By Alexandre L. M. Levada

rag benchmarks

arXiv Machine Learning

Aug 6

Breaking the Periodicity Assumption: Robust Tensorial Multi-View Clustering via Graph-Spectral Low-Rank Learning

arXiv:2607. 25295v2 Announce Type: replace Abstract: Tensorial multi-view clustering (TMC) has achieved strong performance due to its ability to capture high-order correlations across multiple views.

By Jintian Ji, Xingsu Li, Songhe Feng

benchmarks

Related stories

FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling

A Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering

High-dimensional nonparametric changepoint detection via low-rank degree-two density projection

Low-Rank Dependence Decomposition via Accelerated Symmetric Non-negative Matrix Factorization

Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning

Breaking the Periodicity Assumption: Robust Tensorial Multi-View Clustering via Graph-Spectral Low-Rank Learning