Discriminant Analysis

Discriminant analysis is a statistical method used for classifying data points into predefined categories by maximizing the separation between classes. Current research focuses on enhancing discriminant analysis techniques, particularly Linear Discriminant Analysis (LDA), through regularization, handling missing data, and integrating deep learning architectures to improve classification accuracy and robustness, especially in high-dimensional or non-Gaussian data scenarios. These advancements are significant for various applications, including medical image analysis, speaker recognition, and other fields where accurate and interpretable classification is crucial. The development of more efficient and robust algorithms, along with improved methods for handling missing data and non-linear relationships, are key themes in ongoing research.

Papers

October 30, 2024

Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks
Axel Klawonn, Martin Lanser, Janine Weber
Convolutional Neural Network Image Classification CNN Model Discriminant Analysis Global Deep

October 5, 2024

Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model
Maria Heitmeier, Valeria Schmidt, Hendrik P.A. Lensch, R. Harald Baayen
Deep Learning Model Discriminant Analysis Deep Learning Network Discriminative Deep Linear Mapping

September 9, 2024

Validation of Practicality for CSI Sensing Utilizing Machine Learning
Tomoya Tanaka, Ayumu Yabuki, Mizuki Funakoshi, Ryo Yonemoto
External Validation Channel State Information Practical Application Discriminant Analysis Spatial Generalization Human Posture

June 30, 2024

Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretability
Tuan L. Vo, Uyen Dang, Thu Nguyen
Classification Code High Explainability Imputation Algorithm Missing Data Discriminant Analysis Linear Discriminant Analysis

May 23, 2024

Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique
Qishi Zhan, Dan Sun, Erdi Gao, Yuhan Ma, Yaxin Liang, Haowei Yang
Deep Learning Hyperspectral Image Feature Extraction Linear Classifier Discriminant Analysis Unsupervised Medical

February 20, 2024

Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej, Olaf Wolkenhauer
Classification Code Multiclass Classification Missing Data Discriminant Analysis Multivariate Approach Functional Principal Component

February 14, 2024

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification
Agus Hartoyo, Jan Argasiński, Aleksandra Trenk, Kinga Przybylska, Anna Błasiak, Alessandro Crimi
Hessian Matrix Covariance Matrix Discriminant Analysis Covariance Conditioning Improved Classification Class Separability

February 6, 2024

A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
Zhengbo Wang, Jian Liang, Lijun Sheng, Ran He, Zilei Wang, Tieniu Tan
Zero Shot Unsupervised Learning Adaptation Concern Contrastive Language Image Shot Classification Training Free Strong Baseline Discriminant Analysis

January 31, 2024

Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator
Maaz Mahadi, Tarig Ballal, Muhammad Moinuddin, Tareq Y. Al-Naffouri, Ubaid M. Al-Saggaf
Covariance Matrix Ridge Regression Discriminant Analysis Linear Discriminant Analysis

January 2, 2024

Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms
Flavio P. Loss, Pedro H. da Cunha, Matheus B. Rocha, Madson Poltronieri Zanoni, Leandro M. de Lima, Isadora Tavares Nascimento, Isabella Rezende, Tania R. P. Canuto, Luciana de Paula Vieira, Renan Rossoni, Maria C. S. Santos, Patricia Lyra Frasson, Wanderson Romão, Paulo R. Filgueiras, Renato A. Krohling
Machine Learning Algorithm Skin Lesion Discriminant Analysis Skin Cancer Vivo Imaging

December 7, 2023

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures
Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin
Linear Probing Joint Framework Transferable Representation Discriminant Analysis SSL Algorithm Covariance Alignment Linear Discriminant Analysis

November 23, 2023

Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data
Melodee Montgomery, Prosenjit Chatterjee, John Jenkins, Kaushik Roy
Empirical Evaluation Discriminant Analysis Machine Learning Classifier Naive Bayes Touch Sensor Deep Neural Network Classifier Touch Dynamic

November 18, 2023

Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data
Binod Regmi, Chiranjibi Shah
Machine Learning General Analysis Classification Algorithm Discriminant Analysis Fetal Head Ultrasound Fetal Health

November 13, 2023

FEMDA: a unified framework for discriminant analysis
Pierre Houdouin, Matthieu Jonckheere, Frederic Pascal
Unified Framework Maximum Likelihood Cyclic Symmetry Discriminant Analysis Non Gaussian Quadratic Discriminant Analysis

October 17, 2023

Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data
Nicolas Heintz, Tom Francart, Alexander Bertrand
Discriminant Analysis Probabilistic Linear Discriminant Analysis Linear Discriminant Analysis

October 12, 2023

Divorce Prediction with Machine Learning: Insights and LIME Interpretability
Md Manjurul Ahsan
Machine Learning DCU Insight AQ Discriminant Analysis Local Interpretable Model Agnostic Explanation Divorce Predictor

August 17, 2023

Graph Neural Network Backend for Speaker Recognition
Liang He, Ruida Li, Mengqi Niu
Graph Neural Network Speaker Recognition Discriminant Analysis Probabilistic Linear Discriminant Analysis Back End

July 21, 2023

XLDA: Linear Discriminant Analysis for Scaling Continual Learning to Extreme Classification at the Edge
Karan Shah, Vishruth Veerendranath, Anushka Hebbar, Raghavendra Bhat
Continual LEArning Class Incremental Learning Extreme Edge Discriminant Analysis Probabilistic Linear Discriminant Analysis Extreme Classification Linear Discriminant Analysis

July 6, 2023

DSARSR: Deep Stacked Auto-encoders Enhanced Robust Speaker Recognition
Zhifeng Wang, Chunyan Zeng, Surong Duan, Hongjie Ouyang, Hongmin Xu
Speaker Recognition Discriminant Analysis Stacked Autoencoder

July 4, 2023

FEMDA: Une m\'ethode de classification robuste et flexible
Pierre Houdouin, Matthieu Jonckheere, Frederic Pascal
Native Robustness Discriminant Analysis Non Gaussian Robust Classification Quadratic Discriminant Analysis

Discriminant Analysis

Papers

Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks

Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model

Validation of Practicality for CSI Sensing Utilizing Machine Learning

Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretability

Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique

Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification

A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation

Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator

Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures

Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data

Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data

FEMDA: a unified framework for discriminant analysis

Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data

Divorce Prediction with Machine Learning: Insights and LIME Interpretability

Graph Neural Network Backend for Speaker Recognition

XLDA: Linear Discriminant Analysis for Scaling Continual Learning to Extreme Classification at the Edge

DSARSR: Deep Stacked Auto-encoders Enhanced Robust Speaker Recognition

FEMDA: Une m\'ethode de classification robuste et flexible