Sparse Classification

Sparse classification focuses on building efficient and interpretable classification models by selecting only the most relevant features from high-dimensional data. Current research emphasizes developing faster algorithms, such as those based on majorization-minimization or splicing iteration, to handle massive datasets and high-dimensional feature spaces, often within distributed computing frameworks. This work addresses challenges like catastrophic forgetting in online learning and communication bottlenecks in distributed settings, aiming for improved model throughput and accuracy. The resulting sparse models offer advantages in computational efficiency, interpretability, and reduced storage requirements, impacting fields like bioinformatics and precision medicine where high-dimensional data is prevalent.

Papers

November 5, 2024

Navigating Extremes: Dynamic Sparsity in Large Output Space
Nasib Ullah, Erik Schultheis, Mike Lasby, Yani Ioannou, Rohit Babbar
Dynamic Sparse Training Output Space Extreme Value Sparse Matrix Multiplication Dynamic Sparsity Sparse Classification

September 28, 2024

Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning
Xinrui Wang, Chuanxing Geng, Wenhai Wan, Shaoyuan Li, Songcan Chen
Technical Challenge Catastrophic Forgetting Anti Forgetting Online Continual Learning Worst Case User Throughput Constrained Learning Sparse Classification High Myopia

July 8, 2024

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
High Dimension Logistic Regression Statistical Learning Linear Model Parameter Update Sparse Classification Surrogate Likelihood

June 17, 2024

Sparsity-Constraint Optimization via Splicing Iteration
Zezhi Wang, Jin Zhu, Junxian Zhu, Borui Tang, Hongmei Lin, Xueqin Wang
Many Sparse Sparsity Increase Convex Relaxation Sparsity Constraint Alternative Splicing Sparse Classification

June 3, 2024

Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
High Efficiency Many Sparse Empirical Risk Minimization Data Dimensionality Logistic Regression Optimal Weight Sparse Classification Better Approximation

August 31, 2023

Majorization-Minimization for sparse SVMs
Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini, Salla Latva-Aijo, Dominik Narnhofer, Jean-Christophe Pesquet, Sebastian J. Scott, Mahsa Yousefi
Support Vector Machine Majorization Minimization Hinge Loss Sparse Classification

June 7, 2023

Sparse Linear Centroid-Encoder: A Convex Method for Feature Selection
Tomojit Ghosh, Michael Kirby, Karim Karimov
Feature Selection Convex Optimization Sparse Solution Sparse Classification Centroid Encoder

August 11, 2022

RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Elina Francovic-Fontaine, Alexandre Drouin, Jacques Corbeil
Inherent Interpretability Ensemble Learning Interpretable Model Metabolomics Data Sparse Classification Interpretable Ensemble

June 18, 2022

Bioinspired random projections for robust, sparse classification
Nina Dekoninck Bruhin, Bryn Davies
Sparse Representation Bio Inspired Random Projection Signal Representation Sparse Classification

February 23, 2022

Fast Sparse Classification for Generalized Linear and Additive Models
Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin
Many Sparse Interpretable Model Generalized Linear Model Additive Model Fast Classification Sparse Classification Sparse Logistic Regression

December 21, 2021

Max-Margin Contrastive Learning
Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian
Contrastive Learning Unsupervised Visual Representation Learning Sparse Classification Margin Contrastive Effective Unsupervised