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

November 30, 2022

DimenFix: A novel meta-dimensionality reduction method for feature preservation
Qiaodan Luo, Leonardo Christino, Fernando V Paulovich, Evangelos Milios
High Dimensional Dimensionality Reduction Feature Preservation

November 27, 2022

Identifying Chemicals Through Dimensionality Reduction
Emile Anand, Charles Steinhardt, Martin Hansen
Dimensionality Reduction Drug Related Metabolomics Data Chemical Patent

November 21, 2022

A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis
Dong Min Roh, Zhaojun Bai, Ren-Cang Li
Optimal Transport Dimensionality Reduction Discriminant Analysis Wasserstein 2 Nonlinear Principal Component Analysis

November 20, 2022

Graceful Forgetting II. Data as a Process
Alain de Cheveigné
Raw Data Dimensionality Reduction Anti Forgetting Complex Process Large Scale Data Data Structure Word Level Rescaling Computational Account

November 18, 2022

Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction
Montgomery Flora, Corey Potvin, Amy McGovern, Shawn Handler
Machine Learning Machine Learning Model Dimensionality Reduction Large Relevance Improvement Feature Importance Model Explanation Explainability Method Explanation Method Explanation Faithfulness

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

November 11, 2022

Inverse Kernel Decomposition
Chengrui Li, Anqi Wu
Dimensionality Reduction

November 9, 2022

Minimalist Data Wrangling with Python
Marek Gagolewski
Dimensionality Reduction Data Science Exploratory Data Analysis

November 1, 2022

A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch
Classification Code Hyperspectral Image Dimensionality Reduction Mutual Information Support Vector Machine Hyperspectral Image Classification Hyperspectral Benchmark Gray Level Co Occurrence Matrix

October 30, 2022

Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity
Benyamin Ghojogh, Smriti Sharma
Dimensionality Reduction Supervised Dimensionality Reduction Metric Tensor

October 29, 2022

Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images
Asma Elmaizi, Maria Merzouqi, Elkebir Sarhrouni, Ahmed hammouch, Chafik Nacir
Classification Code Hyperspectral Image Dimensionality Reduction Support Vector Machine Information Redundancy

October 27, 2022

Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier
Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch, Nacir Chafik
Hyperspectral Image Dimensionality Reduction Hyperspectral Image Classification Hyperspectral Datasets Support Vector Hyperspectral Sensor

October 26, 2022

October 25, 2022

October 22, 2022

An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images
Elkebir Sarhrouni, Ahmed Hammouch, Driss Aboutajdine
Classification Code Practical Algorithm Feature Selection Hyperspectral Image Dimensionality Reduction Mutual Information Domain Feature

October 18, 2022

A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing
Elkebir Sarhrouni, Ahmed Hammouch, Driss Aboutajdine
General Analysis Hyperspectral Image Remote Sensing Dimensionality Reduction Critical Synthesis High Dimension Hyperspectral Imaging User Interface

October 15, 2022

Transformer-based dimensionality reduction
Ruisheng Ran, Tianyu Gao, Bin Fang
Vision Transformer Dimensionality Reduction Dimensional Embeddings

Dimensionality Reduction

Papers

DimenFix: A novel meta-dimensionality reduction method for feature preservation

Identifying Chemicals Through Dimensionality Reduction

A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis

Graceful Forgetting II. Data as a Process

Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction

Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection

Inverse Kernel Decomposition

Minimalist Data Wrangling with Python

A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information

Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity

Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images

Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier

A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation

A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images

Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information

A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy

An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images

A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing

Transformer-based dimensionality reduction