Dimensionality Reduction

Dimensionality reduction aims to transform high-dimensional data into lower-dimensional representations while preserving essential information, facilitating data visualization, analysis, and efficient processing. Current research emphasizes developing novel algorithms, including those based on neural networks (autoencoders, generative adversarial networks), graph neural networks, and adaptations of classical methods like PCA and t-SNE, to improve the accuracy and efficiency of dimensionality reduction for various data types (e.g., time series, images, graphs). These advancements are crucial for addressing the "curse of dimensionality" in diverse fields, ranging from medical image analysis and climate modeling to improving the performance and scalability of machine learning models.

Papers

April 27, 2023

On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena
Roy R. Lederman, Bogdan Toader
Unsupervised Learning Dimensionality Reduction Manifold Learning Low Dimensional Manifold Online Comment Subterranean Environment Physical Mechanism

April 17, 2023

Wireless Channel Charting: Theory, Practice, and Applications
Paul Ferrand, Maxime Guillaud, Christoph Studer, Olav Tirkkonen
Financial Application Theoretical Understanding Dimensionality Reduction Practice Mode Channel State Information Dimensional Space Next Generation Wireless Network Channel Charting

April 15, 2023

Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
Dimensionality Reduction Variational Method PCa Detection Low Dimensional Latent Probabilistic Inference Algorithm

April 12, 2023

Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning
Miguel Conceição, Alberto Krone-Martins, Antonio da Silva, Ángeles Moliné
Machine Learning Dimensionality Reduction Density Field N Body Simulation

March 31, 2023

Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data
Qian Wang, Kamran Paynabar
Point Cloud Dimensionality Reduction Low Dimensional Manifold Point Cloud Data Tensor Regression

March 26, 2023

Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Dimensionality Reduction Data Dimensionality

March 24, 2023

Feature Space Sketching for Logistic Regression
Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas
Feature Selection Dimensionality Reduction Logistic Regression

March 17, 2023

An evaluation framework for dimensionality reduction through sectional curvature
Raúl Lara-Cabrera, Ángel González-Prieto, Diego Pérez-López, Diego Trujillo, Fernando Ortega
Dimensionality Reduction Dimension Reduction Evaluation Framework Gaussian Curvature

March 16, 2023

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction
Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang, Yang-chih Fu, Anders Ynnerman, Kwan-Liu Ma
Neural Network Representation Learning Dimensionality Reduction Visual Analytics Non Linear Composite Variable Construction Multivariate Statistical Network Monitoring

March 15, 2023

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
Quanfeng Xu, Shiyin Shen, Rafael S. de Souza, Mi Chen, Renhao Ye, Yumei She, Zhu Chen, Emille E. O. Ishida, Alberto Krone-Martins, Rupesh Durgesh
Domain Adaptation Dimensionality Reduction Feature Wise Variational Auto Mathematical Morphology Galaxy Image

February 22, 2023

nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces
Richard Connor, Lucia Vadicamo
Dimensionality Reduction High Dimensional Space Euclidean Space Hilbert Space Multidimensional Scaling

January 31, 2023

Learning Topology-Preserving Data Representations
Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin, Evgeny Burnaev, Serguei Barannikov
LeArning Abstract Dimensionality Reduction Low Dimensional Representation Data Representation Latent Topology

January 26, 2023

SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction
Leland Barnard, Farwa Ali, Hugo Botha, David T. Jones
Many Sparse Dimensionality Reduction Feature Extraction Interpretable Feature Compressive Learning

January 22, 2023

Information loss from dimensionality reduction in 5D-Gaussian spectral data
A. Schelle, H. Lüling
Dimensionality Reduction Shannon Entropy Spectral Information Information Loss Spectral Data

January 17, 2023

DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking
Yu Gao, Xi Xu, Tianji Jiang, Siyuan Chen, Yi Yang, Yufeng Yue, Mengyin Fu
LeArning Abstract High Dimensional Estimation Task Dimensionality Reduction Dimension Reduction 2 Dimensional Object Detection

January 12, 2023

Fair and skill-diverse student group formation via constrained k-way graph partitioning
Alexander Jenkins, Imad Jaimoukha, Ljubisa Stankovic, Danilo Mandic
Dimensionality Reduction Unsupervised Algorithm Graph Partitioning Team Formation

January 10, 2023

Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization
Hao Wang
General Analysis Recommender System Dimensionality Reduction Matrix Factorization Movie Recommendation Geometric Understanding

January 1, 2023

Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
Idan Cohen, Ofir Lindenbaum, Sharon Gannot
Dimensionality Reduction Microphone Array Acoustic Feature Room Reverberation Time Difference of Arrival

December 18, 2022

The Underlying Correlated Dynamics in Neural Training
Rotem Turjeman, Tom Berkov, Ido Cohen, Guy Gilboa
Neural Network GAN Model Dimensionality Reduction Regularization Model Training Dynamic Dynamic Mode Decomposition

November 30, 2022

Universal Feature Selection Tool (UniFeat): An Open-Source Tool for Dimensionality Reduction
Sina Tabakhi, Parham Moradi
Feature Selection Dimensionality Reduction Open Source

Dimensionality Reduction

Papers

On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena

Wireless Channel Charting: Theory, Practice, and Applications

Dimensionality Reduction as Probabilistic Inference

Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning

Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data

Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis

Feature Space Sketching for Logistic Regression

An evaluation framework for dimensionality reduction through sectional curvature

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation

nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces

Learning Topology-Preserving Data Representations

SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction

Information loss from dimensionality reduction in 5D-Gaussian spectral data

DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking

Fair and skill-diverse student group formation via constrained k-way graph partitioning

Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization

Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction

The Underlying Correlated Dynamics in Neural Training

Universal Feature Selection Tool (UniFeat): An Open-Source Tool for Dimensionality Reduction