Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Regularized tensor factorization for multi-modality medical image classification
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Multi-modal multi-task learning for joint prediction of clinical scores in Alzheimer's disease
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Predicting clinical scores using semi-supervised multimodal relevance vector regression
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
MultiCost: multi-stage cost-sensitive classification of Alzheimer's disease
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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We study the problem of classifying mild Alzheimer's disease (AD) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification of AD related pathologies. Several recent papers have demonstrated that such classification is possible with MR or PET images, using machine learning methods such as SVM and boosting. These algorithms learn the classifier using one type of image data. However, AD is not well characterized by one imaging modality alone, and analysis is typically performed using several image types --- each measuring a different type of structural/functional characteristic. This paper explores the AD classification problem using multiple modalities simultaneously . The difficulty here is to assess the relevance of each modality (which cannot be assumed a priori), as well as to optimize the classifier. To tackle this problem, we utilize and adapt a recently developed idea called Multi-Kernel learning (MKL). Briefly, each imaging modality spawns one (or more kernels) and we simultaneously solve for the kernel weights and a maximum margin classifier. To make the model robust, we propose strategies to suppress the influence of a small subset of outliers on the classifier --- this yields an alternative minimization based algorithm for robust MKL. We present promising multi-modal classification experiments on a large dataset of images from the ADNI project.