Jing Wang

Open-with-Cursor. Add Cursor editor options to the Windows context menu for files, folders, and folder backgrounds.

Douban-books-results. Douban books grabbed from doulists, series, and tags.

Douban-books-2020. Douban books from doulists and tags.

Open-with-Antigravity. Add Antigravity editor options to the Windows context menu for files, folders, and folder backgrounds.

2DPCAL1-S. Scripts for the paper: 2DPCA with L1-norm for simultaneously robust and sparse modelling.

Douban-books-2017. Douban books.

GWC. Scripts for the paper: Generation of individual whole-brain atlases with resting-state fMRI data using simultaneous graph computation and parcellation.

G2DPCA. Scripts for the paper: Generalized 2-D principal component analysis by Lp-norm for image analysis.

SLIC-individual. Scripts for the paper: Parcellating whole brain for individuals by simple linear iterative clustering.

MNIST-classification-example. Classify the MNIST data by LIBSVM in Matlab.

Douban-books-crawler-2020. 豆瓣读书爬虫

fMRI-classification-example. Pattern classification with fMRI data. Reproduced from Poldrack's repository by Matlab.

SLIC_atlas. Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.

MNIST-classification-example-3. Classify the MNIST data by LIBSVM in Python.

PD-MCI-Classification. Scripts for the paper: Diagnostic Classification of Mild Cognitive Impairment in Parkinson's Disease Using Subject-Level Stratified Machine-Learning Analysis

PD-regression-82subs. 论文：基于贝叶斯优化的支持向量回归预测帕金森病严重程度研究

DB2DPCA. Scripts for the paper: Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

VALSE-Webinar-Slides-2. Download slides from: http://valser.org/webinar/slide/

SLIC_2. Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.

G2DPCA_demo_1. Scripts for the paper: Generalized 2-D principal component analysis by Lp-norm for image analysis.

SLIC-individual-light. A light version of SLIC_individual which consumes less memory.