Imagen de Portada

Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Fischer, Jonas, Ma, Rong
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Low-dimensional embeddings of high-dimensional data
por: de Bodt, Cyril, et al.
Publicado: (2025)

Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators
por: Ding, Xiucai, et al.
Publicado: (2024)

Reproduction of IVFS algorithm for high-dimensional topology preservation feature selection
por: Wang, Zihan
Publicado: (2024)

Identifying recurrent flows in high-dimensional dissipative chaos from low-dimensional embeddings
por: Beck, Pierre, et al.
Publicado: (2026)

The cell as a token: high-dimensional geometry in language models and cell embeddings
por: Gilpin, William
Publicado: (2025)

Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation
por: Gildenblat, Jacob, et al.
Publicado: (2025)

Generative modeling through internal high-dimensional chaotic activity
por: Fournier, Samantha J., et al.
Publicado: (2024)

On high-dimensional modifications of the nearest neighbor classifier
por: Ghosh, Annesha, et al.
Publicado: (2024)

Machine learning and high dimensional vector search
por: Douze, Matthijs
Publicado: (2025)

Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction
por: Kneifl, Jonas, et al.
Publicado: (2023)

A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spaces
por: Boschi, Tobia, et al.
Publicado: (2024)

High dimensional Bayesian Optimization via Condensing-Expansion Projection
por: Lu, Jiaming, et al.
Publicado: (2024)

Robust fuzzy clustering for high-dimensional multivariate time series with outlier detection
por: Ma, Ziling, et al.
Publicado: (2025)

Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding
por: Sasaki, Hiroaki
Publicado: (2024)

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics
por: Wang, Zijian, et al.
Publicado: (2025)

Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
por: Corenflos, Adrien, et al.
Publicado: (2024)

Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
por: Burt, David R., et al.
Publicado: (2025)

Heterogeneous transfer learning for high-dimensional regression with feature mismatch
por: Chang, Jae Ho, et al.
Publicado: (2024)

Adaptive debiased SGD in high-dimensional GLMs with streaming data
por: Han, Ruijian, et al.
Publicado: (2024)

Improving sample efficiency of high dimensional Bayesian optimization with MCMC
por: Yi, Zeji, et al.
Publicado: (2024)

Fubini Study geometry of representation drift in high dimensional data
por: Tozzi, Arturo
Publicado: (2026)

A new model for natural groupings in high-dimensional data
por: Boutin, Mireille, et al.
Publicado: (2019)

Classification of high-dimensional data with spiked covariance matrix structure
por: Chen, Yin-Jen, et al.
Publicado: (2021)

Contrastive representations of high-dimensional, structured treatments
por: Andreu, Oriol Corcoll, et al.
Publicado: (2024)

Consistent support recovery for high-dimensional diffusions
por: Marushkevych, Dmytro, et al.
Publicado: (2025)

Low-dimensional adaptation of diffusion models: Convergence in total variation
por: Liang, Jiadong, et al.
Publicado: (2025)

How high is `high'? Rethinking the roles of dimensionality in topological data analysis and manifold learning
por: Sansford, Hannah, et al.
Publicado: (2025)

Predicting two-dimensional spatiotemporal chaotic patterns with optimized high-dimensional hybrid reservoir computing
por: Nakano, Tamon, et al.
Publicado: (2025)

Scaling and renormalization in high-dimensional regression
por: Atanasov, Alexander, et al.
Publicado: (2024)

ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets
por: Shen, Yafei, et al.
Publicado: (2022)

Learnability of high-dimensional targets by two-parameter models and gradient flow
por: Yarotsky, Dmitry
Publicado: (2024)

Latent variable model for high-dimensional point process with structured missingness
por: Sinelnikov, Maksim, et al.
Publicado: (2024)

Features are fate: a theory of transfer learning in high-dimensional regression
por: Tahir, Javan, et al.
Publicado: (2024)

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
por: González-Duque, Miguel, et al.
Publicado: (2024)

Improved high-dimensional estimation with Langevin dynamics and stochastic weight averaging
por: Wei, Stanley, et al.
Publicado: (2026)

A general technique for approximating high-dimensional empirical kernel matrices
por: Kaushik, Chiraag, et al.
Publicado: (2025)

Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings
por: Ma, Rong, et al.
Publicado: (2025)

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions
por: Cornacchia, Elisabetta, et al.
Publicado: (2025)

Latent mixed-effect models for high-dimensional longitudinal data
por: Ong, Priscilla, et al.
Publicado: (2024)

Modeling high dimensional point clouds with the spherical cluster model
por: Cazals, Frédéric, et al.
Publicado: (2025)