1 / 16

Shridharan Chandramouli Michelle Hromatka Yang Shen Sumedha Singla 5 December 2013

Kernel selection and dimensionality reduction in SVM classification of Autism Spectrum Disorders (ASDs). Shridharan Chandramouli Michelle Hromatka Yang Shen Sumedha Singla 5 December 2013. Motivation . What is an ASD? 1in 88 children today What makes the research difficult?.

quinto
Download Presentation

Shridharan Chandramouli Michelle Hromatka Yang Shen Sumedha Singla 5 December 2013

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Kernel selection and dimensionality reduction in SVM classification of Autism Spectrum Disorders (ASDs) ShridharanChandramouli Michelle Hromatka Yang Shen SumedhaSingla 5 December 2013

  2. Motivation • What is an ASD? • 1in 88 children today • What makes the research difficult?

  3. Problem Statement • Determine the best combination of kernel for SVM and dimensionality reduction for high dimensional data for classification of ASDs using resting state f-MRI • Goal of research: way to interpret the data

  4. Data • Reducing data to meaningful features • ABIDE dataset • 101 subjects • 34,716 features

  5. Data

  6. Basic Method • Dimensionality Reduction • For each subject S in dataset train SVM using all other subjects classify S based on model

  7. Dimensionality Reduction • PCA – principal component analysis • Fixed slope regression • Others – SVM-RFE, manual selection

  8. Fixed Slope Regression • Anderson, et. al., BRAIN: A journal of Neurology, October 2011 For data point age

  9. Kernels • RBF • Polynomial • Sigmoid • Linear

  10. Basic Method • Dimensionality Reduction? • For each subject S in dataset Regress out age in subjects scale data determine parameters for SVM train SVM using all other subjects classify S based on model store label

  11. Results • Accuracy • Sensitivity • Specificity

  12. Results (%) Using Fixed Slope Regression Using RBF Kernel

  13. Other results in the field • Anderson – fMRI, basic threshold • Ghiassian – multi-site data • Wee – structural MRI volumes

  14. Future work • Combine structural/fMRI data • Multisite extension • Medical process, multi-test process

  15. Sources • Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N., ... & Lainhart, J. E. (2011). Functional connectivity magnetic resonance imaging classification of autism. Brain, 134(12), 3742-3754. • Ghiassian, S., Greiner, R., Jin, P., Brown, M.R.G. (2013). Learning to classify psychiatric disorders based on fMR Images: Autism vs. healthy and ADHD vs. Healthy. • Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., ... & Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665-678. • Wee, C. Y., Wang, L., Shi, F., Yap, P. T., & Shen, D. (2013). Diagnosis of autism spectrum disorders using regional and interregional morphological features. Human Brain Mapping. • Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapnik, V. (2000, December). Feature selection for SVMs. In NIPS (Vol. 12, pp. 668-674).

  16. PCA • Data • N x d matrix, N = number of subjects, d is dimension of the data • Mapping such that >> t is the principal component “scores”

More Related