Dimensional Space

Dimensional reduction techniques aim to represent high-dimensional data in lower-dimensional spaces while preserving essential features and relationships. Current research focuses on developing and improving algorithms like variational autoencoders, t-SNE, UMAP, and neural operators, often incorporating techniques like manifold learning and clustering to achieve efficient and interpretable low-dimensional embeddings. These advancements are crucial for tackling the "curse of dimensionality" in various fields, enabling improved data visualization, enhanced machine learning performance, and facilitating knowledge discovery in complex datasets across diverse scientific domains and practical applications. The development of explainable and robust methods remains a key focus.

Papers

November 17, 2022

Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection
Yang Yang, Hongjian Sun, Jialei Gong, Di Yu
Dimensionality Reduction Singular Value Decomposition Projection Bias Dimensional Space Manifold Approximation

October 10, 2022

ParaDime: A Framework for Parametric Dimensionality Reduction
Andreas Hinterreiter, Christina Humer, Bernhard Kainz, Marc Streit
New Framework Dimensionality Reduction High Dimensional Data Dimensional Space

August 27, 2022

Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
Nicolò Botteghi, Mannes Poel, Christoph Brune
Deep Reinforcement Learning Narrative Review Unsupervised Representation Learning Dimensional Space

August 10, 2022

June 4, 2022

SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
Zihan Wang, Ruimin Chen, Mengxuan Liu, Guanfang Dong, Anup Basu
3D Human 3D Human Pose Estimation Dimensional Space 2 Dimensional Pose Multi Dimensional Re Projection

February 5, 2022

Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
Roozbeh Yousefzadeh
Image Classification Feature Space Digital Image Decision Boundary Low Dimensional Manifold Image Classification Model Convex Hull Dimensional Space Deep Function

November 30, 2021

CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data
Yunhui Guo, Haoran Guo, Stella Yu
Dimensionality Reduction Interactive Visualization Dimensional Embeddings Hyperbolic Embeddings Dimensional Space Hyperbolic Data

November 26, 2021

Dimension Reduction with Prior Information for Knowledge Discovery
Anh Tuan Bui
High Dimensional Data Dimension Reduction Knowledge Discovery Dimensional Space Multidimensional Scaling

November 8, 2021

Efficient estimates of optimal transport via low-dimensional embeddings
Patric M. Fulop, Vincent Danos
Optimal Transport Dimensional Embeddings Dimensional Space Projection Matrix Probability Measure