# Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification

Source: [Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification](<https://www.sota2.com/research/paper/68fa65a01e209131cea0b57b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:31:37.819Z

Authors: Qiang Li, Bo Xie, Jane You, Wei Bian, Dacheng Tao

Published: 2019-04-17T06:16:59.000Z

## Abstract

In this paper, we present correlated logistic (CorrLog) model for multilabel image classification. CorrLog extends conventional logistic regression model into multilabel cases, via explicitly modeling the pairwise correlation between labels. In addition, we propose to learn the model parameters of CorrLog with elastic net regularization, which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification. CorrLog can be efficiently learned, though approximately, by regularized maximum pseudo likelihood estimation, and it enjoys a satisfying generalization bound that is independent of the number of labels. CorrLog performs competitively for multilabel image classification on benchmark data sets MULAN scene, MIT outdoor scene, PASCAL VOC 2007, and PASCAL VOC 2012, compared with the state-of-the-art multilabel classification algorithms.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.08098>)

[PDF](<https://arxiv.org/pdf/1904.08098.pdf>)