# Multi-View Matrix Completion for Multi-Label Image Classification

## TL;DR

This paper introduces a multi-view matrix completion framework that effectively combines multiple features for semi-supervised multi-label image classification, improving accuracy and robustness over single-view methods.

## Contribution

It proposes a novel multi-view matrix completion approach with weighted feature combination and cross-validation for label prediction in multi-label image classification.

## Key findings

- MVMC outperforms single-view methods on PASCAL VOC'07 and MIR Flickr datasets.
- The approach effectively exploits complementary features for better label prediction.
- Using AP loss enhances robustness in multi-label classification.

## Abstract

There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has several distinct advantages, including robustness to missing data and background noise in both feature and label space. However, it is limited by only considering data represented by a single-view feature, which cannot precisely characterize images containing several semantic concepts. To utilize multiple features taken from different views, we have to concatenate the different features as a long vector. But this concatenation is prone to over-fitting and often leads to very high time complexity in MC based image classification. Therefore, we propose to weightedly combine the MC outputs of different views, and present the multi-view matrix completion (MVMC) framework for transductive multi-label image classification. To learn the view combination weights effectively, we apply a cross validation strategy on the labeled set. In the learning process, we adopt the average precision (AP) loss, which is particular suitable for multi-label image classification. A least squares loss formulation is also presented for the sake of efficiency, and the robustness of the algorithm based on the AP loss compared with the other losses is investigated. Experimental evaluation on two real world datasets (PASCAL VOC' 07 and MIR Flickr) demonstrate the effectiveness of MVMC for transductive (semi-supervised) multi-label image classification, and show that MVMC can exploit complementary properties of different features and output-consistent labels for improved multi-label image classification.