Deformation Analysis for Shape Based Classification
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Two-view feature generation model for semi-supervised learning
Proceedings of the 24th international conference on Machine learning
A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
MKL for Robust Multi-modality AD Classification
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
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This paper presents a general discriminative dimensionality reduction framework for multi-modal image-based classification in medical imaging datasets. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being clinically interpretable. We propose a framework based on regularized tensor decomposition. We show that different variants of tensor factorization imply various hypothesis about data. Inspired by the idea of multiview dimensionality reduction in machine learning community, two different kinds of tensor decomposition and their implications are presented. We have validated our method on a multi-modal longitudinal brain imaging study. We compared this method with a publically available classification software based on SVM that has shown state-of-the-art classification rate in number of publications.