Large Margin Multi-modal Multi-task Feature Extraction for Image Classification
This work addresses the challenge of handling correlated and noisy multi-modal features in image analysis, offering an incremental improvement over existing single-modal or multi-task methods.
The paper tackles the problem of high-dimensional multi-modal feature extraction for image classification by proposing a large margin multi-modal multi-task framework (LM3FE) that learns feature extraction matrices and combination coefficients, resulting in demonstrated effectiveness and superiority on two real-world datasets.
The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method.