Dimensionality Reduction

4 items

RESEARCH↑ trendingReddit r/MachineLearning·4/9/2026

[P] PCA before truncation makes non-Matryoshka embeddings compressible: results on BGE-M3 [P]

O conteúdo explora a técnica de aplicar PCA antes da truncagem de dimensões para comprimir embeddings, demonstrando que esta abordagem preserva a qualidade dos vetores (similaridade de cosseno) muito melhor do que a truncagem ingênua, especialmente para modelos não Matryoshka como BGE-M3. A técnica também é comparada a outros métodos de compressão, como quantização, mostrando resultados competitivos em termos de desempenho e taxa de compressão.

Dimensionality Reduction PCA Embedding Compression

DOCDEV.to AI·4/24/2026

Visualizing Data using GTSNE

This content explores data visualization using GTSNE, an advanced technique for reducing the dimensionality of complex datasets. It details how to apply GTSNE to reveal intrinsic patterns and structures in high-dimensional data, facilitating interpretation and analysis.

Dimensionality Reduction machine learning AI data visualization

RESEARCHDEV.to AI·5/2/2026

Deep convolutional recurrent autoencoders for learning low-dimensional featuredynamics of fluid systems

This content discusses the application of deep convolutional recurrent autoencoders to learn low-dimensional feature dynamics in fluid systems.

Dimensionality Reduction fluid dynamics deep learning autoencoders

RESEARCHarXiv CS.LG·21d ago

AdaGraph: A Graph-Native Clustering Algorithm That Overcomes the Curse of Dimensionality and Enables Scientific Discovery

AdaGraph is a graph-native clustering algorithm from the Structure-Centric Machine Learning (SC-ML) paradigm, which fundamentally dissolves the curse of dimensionality by replacing geometry-centric computation with topology-based computation. Operating within kNN graph topology, it requires no a priori specification of cluster numbers, handles noise, and scales effectively.

Dimensionality Reduction machine learning graph theory Clustering Algorithms