Latent Class

Latent class analysis (LCA) is a statistical technique used to identify unobserved subgroups (latent classes) within a population based on observed categorical data. Current research focuses on extending LCA to handle complex data structures, such as multi-layer data and hierarchical data, and incorporating it into other machine learning frameworks like federated learning and few-shot learning. These advancements improve the interpretability and predictive power of models, particularly in applications involving noisy or imbalanced data, leading to more robust and fair AI systems across various domains including healthcare, social sciences, and recommender systems. Furthermore, research is actively exploring efficient algorithms, such as spectral methods and Bayesian approaches, to improve the scalability and accuracy of LCA.

Papers

October 28, 2024

Adaptive Transfer Clustering: A Unified Framework
Yuqi Gu, Zhongyuan Lyu, Kaizheng Wang
Unified Framework Gaussian Mixture Model Latent Class

October 17, 2024

On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods
Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
NCD Method DiNO Mix Latent Prototype Latent Class Representation Collapse

August 10, 2024

Latent class analysis for multi-layer categorical data
Huan Qing
Latent Class Categorical Distribution Response Matrix

June 17, 2024

An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviews
Giuseppe Serra, Peter Tino, Zhao Xu, Xin Yao
Deep Learning Representation Learning Recommender System Latent Class Rating Prediction User Review Latent Label

June 5, 2024

Equivalence Set Restricted Latent Class Models (ESRLCM)
Jesse Bowers, Steve Culpepper
Item Response Theory Novel Bayesian Latent Class

May 29, 2024

Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation
Lianlei Shan, Wenzhang Zhou, Wei Li, Xingyu Ding
Background Knowledge Pre Trained Segmentation Model Latent Class Class Prototype Novel Class Prototype Network

May 24, 2024

Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness
Yingfang Yuan, Kefan Chen, Mehdi Rizvi, Lynne Baillie, Wei Pang
Procedural Fairness Fair AI Social Group Latent Class Fairness Issue Soft Inequality

January 17, 2024

Unlocking Unlabeled Data: Ensemble Learning with the Hui- Walter Paradigm for Performance Estimation in Online and Static Settings
Kevin Slote, Elaine Lee
Machine Learning Ensemble Learning Unlabeled Data Statistical Model Hui Walter Paradigm Performance Estimation Latent Class Multi Population Static Feature

November 24, 2023

Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene
LeArning Abstract Text Modality Shot Learning Shot Classification Latent Class Shot Generalization

November 15, 2023

Time-dependent Probabilistic Generative Models for Disease Progression
Onintze Zaballa, Aritz Pérez, Elisa Gómez-Inhiesto, Teresa Acaiturri-Ayesta, Jose A. Lozano
Disease Progression Latent Class Generative Time Series

October 30, 2023

Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task
Michael John Ilagan
LeArning Abstract Chatbot Response Cross Validation Training Label Latent Class Troll Meme Dialogue Safety

October 28, 2023

Latent class analysis by regularized spectral clustering
Huan Qing
Spectral Clustering Latent Class

September 2, 2023

Tutorial: a priori estimation of sample size, effect size, and statistical power for cluster analysis, latent class analysis, and multivariate mixture models
Edwin S Dalmaijer
Mixture Model Cluster Analysis Sample Size Effect Size Latent Class Subgroup Analysis

October 25, 2022

SepLL: Separating Latent Class Labels from Weak Supervision Noise
Andreas Stephan, Vasiliki Kougia, Benjamin Roth
Weak Label Latent Class Labeling Function

October 17, 2022

Understanding CNN Fragility When Learning With Imbalanced Data
Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh Chawla
Deep Learning Convolutional Neural Network CNN Network Imbalanced Data Latent Class Class Diversity Intrinsic Class

October 8, 2022

An Ordinal Latent Variable Model of Conflict Intensity
Niklas Stoehr, Lucas Torroba Hennigen, Josef Valvoda, Robert West, Ryan Cotterell, Aaron Schein
Latent Variable Intensity Confusion Latent Class

June 22, 2022

Federated Latent Class Regression for Hierarchical Data
Bin Yang, Thomas Carette, Masanobu Jimbo, Shinya Maruyama
Hierarchical Latent Hierarchical Datasets Latent Class Federated Learning Model

April 15, 2022

Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Mayee F. Chen, Daniel Y. Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Ré
Contrastive Learning Native Robustness Formality Transfer Supervised Contrastive Learning Better Representation Return Conditioned Supervised Learning Latent Class Conditional Autoencoder

March 8, 2022

Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Wen Yao, Yunyang Zhang, Xiaohu Zheng
Segmentation Based Approach Shot Segmentation Contrast Enhancement Latent Prototype Latent Class

December 27, 2021

Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
Renyu Zhang, Aly A. Khan, Robert L. Grossman, Yuxin Chen
Active Learning Deep Active Learning Latent Class ActIve Learning algORithm Mini Batch Consistency Batch Active Learning Decision Aware