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Xiao Liu
Researcher at Shanghai University
Publications - 7
Citations - 230
Xiao Liu is an academic researcher from Shanghai University. The author has contributed to research in topics: Feature learning & Feature (computer vision). The author has an hindex of 6, co-authored 7 publications receiving 177 citations.
Papers
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Journal ArticleDOI
Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset
TL;DR: A stacked DPN (S-DPN) algorithm is proposed to further improve the representation performance of the original DPN, and S-DPn is applied to the task of texture feature learning for ultrasound based tumor classification with small dataset, suggesting that S- DPN can be a strong candidate for the texture feature representation learning on small ultrasound datasets.
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
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.
Book ChapterDOI
Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset
TL;DR: The experimental results show that the proposed DPN and MKL based feature learning and classification framework DPN-MKL algorithm outperforms the commonly used DL algorithms for ultrasound image based tumor classification on small dataset.
Proceedings ArticleDOI
An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images
TL;DR: To augment the classification accuracy of the breast ultrasound CAD based on texture feature, an Iter-LR-based semi-supervised CRFS significantly outperforms all other algorithms and is compared with LR-CRFS, original supervised CRFS, and principal component analysis.