arxivJul 30bullish

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design

arXiv:2605.11759v2 Announce Type: replace-cross Abstract: Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeling, and systematic design-space exploration. Parametric Model Embedding (PME) addresses this

NLPMDE3 models #dimensionality-reduction #parametric-modeling

arxivJul 24bullish

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607.20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance. However, lightweight methods often struggle with high-dimensional data and typically require careful tuning of multiple hyperpar

CLAU2 models #anomaly-detection #dimensionality-reduction #machine-learning Read on arxiv →

arxivMay 4bullish

Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization

arXiv:2605.00641v1 Announce Type: new Abstract: Both Dimensionality Reduction (DR) and Graph Drawing (GD) aim to visualize abstract, non-linear structures, yet rely on different optimization paradigms. This contrast is evident in Multidimensional Scaling (MDS), which typically depends on the SMACOF

#optimization #dimensionality-reduction #machine-learning Read on arxiv →