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Yan Li
Researcher at Shenzhen University
Publications - 10
Citations - 511
Yan Li is an academic researcher from Shenzhen University. The author has contributed to research in topics: Feature learning & Deep learning. The author has an hindex of 8, co-authored 10 publications receiving 349 citations.
Papers
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Journal ArticleDOI
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
TL;DR: Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.
Journal ArticleDOI
Histopathological Image Classification With Color Pattern Random Binary Hashing-Based PCANet and Matrix-Form Classifier
TL;DR: The experimental results on three color histopathological image datasets show that the proposed C-RBH-PCANet algorithm is superior to the original PCANet and other conventional unsupervised deep learning algorithms, while the best performance is achieved by the proposed feature learning and classification framework that combines C- RBH- PCBanet and matrix-form classifier.
Journal ArticleDOI
Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.
TL;DR: The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR.
Proceedings ArticleDOI
Improving MRI-based diagnosis of Alzheimer's disease via an ensemble privileged information learning algorithm
TL;DR: The experimental results demonstrate that the proposed RBM+ works well as an LUPI algorithm for feature learning, and the ensemble L UPI algorithm is superior to the traditional predictive models for the MRI-based AD diagnosis using the positron emission tomography as the privileged information.
Proceedings ArticleDOI
Multi-modality stacked deep polynomial network based feature learning for Alzheimer's disease diagnosis
TL;DR: A stacked DPN (S- DPN) algorithm is proposed to further improve feature representation and a multi-modality S-DPN (MM-S-DPn) algorithm to fuse multi- modality neuroimaging data and learn more discriminative and robust feature representation for AD classification is proposed.