Efficient and Automatic Subspace Relevance Determination via Multiple Kernel Learning for High-Dimensional Neuroimaging Data

Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data

2014 22nd International Conference on Pattern Recognition ◽

10.1109/icpr.2014.549 ◽

2014 ◽

Author(s):

Andrew D. O'Harney ◽

Andre Marquand ◽

Katya Rubia ◽

Kaylita Chantiluke ◽

Anna Smith ◽

...

Keyword(s):

Multiple Kernel Learning ◽

Kernel Learning ◽

Multiple Kernel ◽

Neuroimaging Data ◽

Multiple Class

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P.029 Classifying mood disorders using multiple kernel learning on multimodal neuroimaging data: translating biological data into a diagnostic tool for depression

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2019.09.095 ◽

2019 ◽

Vol 29 ◽

pp. S40-S41

Author(s):

B. Vai ◽

L. Parenti ◽

C. Cara ◽

C. Verga ◽

I. Bollettini ◽

...

Keyword(s):

Mood Disorders ◽

Diagnostic Tool ◽

Multiple Kernel Learning ◽

Biological Data ◽

Kernel Learning ◽

Multimodal Neuroimaging ◽

Multiple Kernel ◽

Neuroimaging Data

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Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

Remote Sensing ◽

10.3390/rs10101639 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1639 ◽

Cited By ~ 11

Author(s):

Tianming Zhan ◽

Le Sun ◽

Yang Xu ◽

Guowei Yang ◽

Yan Zhang ◽

...

Keyword(s):

Hyperspectral Image ◽

Multiple Kernel Learning ◽

Low Rank ◽

Kernel Learning ◽

Parameter Determination ◽

High Dimensional ◽

Dimensional Manifold ◽

Multiple Kernel ◽

Kernel Parameter ◽

Low Rank Representation

High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic.

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