# Manifold regularized kernel logistic regression for web image annotation

arXiv: https://arxiv.org/abs/1312.6180 · HTML: https://mlsanity.com/paper/1312.6180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2013.

## Built on by (most cited first, 1 of 1)

- [Scalable Kernel Logistic Regression with Nystr"om Approximation: Theoretical Analysis and Application to Discrete Choice Modelling](https://mlsanity.com/paper/2402.06763/) (2024)

## Abstract (arXiv)

With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.