J
Jie Yang
Researcher at Shanghai Jiao Tong University
Publications - 680
Citations - 12772
Jie Yang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Image segmentation & Feature extraction. The author has an hindex of 46, co-authored 629 publications receiving 10558 citations. Previous affiliations of Jie Yang include East China University of Science and Technology & Chinese Ministry of Education.
Papers
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Journal ArticleDOI
Small Target Detection Using Two-Dimensional Least Mean Square (TDLMS) Filter Based on Neighborhood Analysis
Yuan Cao,Ruiming Liu,Jie Yang +2 more
TL;DR: A new TDLMS filter structure and implementation incorporating neighborhood analysis and data fusion is presented, which is capable of acquiring and analyzing more information from the vicinity of the target, leading to a more prominent detection result.
Journal ArticleDOI
Predicting membrane protein types by the LLDA algorithm.
TL;DR: Here, a completely different approach, the so-called LLDA (Local Linear Discriminant Analysis) is introduced to extract the key features from the high-dimensional PsePSSM space, and the dimension-reduced descriptor vector thus obtained is a compact representation of the original high dimensional vector.
Book
Support Vector Machine in Chemistry
TL;DR: This book provides a systematic approach to the principles and algorithms of S VM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology.
Proceedings ArticleDOI
Violent video detection based on MoSIFT feature and sparse coding
TL;DR: This paper employs Motion SIFT (MoSIFT) algorithm to extract the low-level description of a query video and adopts sparse coding scheme to further process the selected MoSIFTs to obtain the highly discriminative video feature.
Journal ArticleDOI
Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma.
TL;DR: The rough set rule-based method applied to predict the degree of malignancy can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks.