Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first ‘repair’ the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer’s Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.
A novel feature descriptor-contourlet Fourier invariant feature, which combine contourlet decomposition and Fourier
transforms and is translation-, rotation-, and scale-invariant, is put forward in this paper. Firstly, the translation and rotation
invariant are achieved by Fourier transform along the circles that around the mass center of the scale-normalized target.
Then statistic parameters of General Gaussian density (GGD) model of each contourlet sub-bands are evaluated. GGD
parameters and contourlet decomposition coefficients are both as the features, which not only with rotation, shift and
scaling invariant, but also with the contourlet inherent property of multi-resolution, local and multi-direction. We present
experimental results using this descriptor in infrared image recognition, and it shows this descriptor is a good choice for
object recognition.
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