H
Haitao Zhao
Researcher at Nanjing University of Posts and Telecommunications
Publications - 139
Citations - 1973
Haitao Zhao is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Fault detection and isolation. The author has an hindex of 17, co-authored 120 publications receiving 1408 citations. Previous affiliations of Haitao Zhao include East China University of Science and Technology & Nanjing University of Science and Technology.
Papers
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A novel incremental principal component analysis and its application for face recognition
TL;DR: Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%.
Journal ArticleDOI
Incremental Linear Discriminant Analysis for Face Recognition
Haitao Zhao,Pong C. Yuen +1 more
TL;DR: Experimental results show that the proposed GSVD-ILDA algorithm gives the same performance as the LDA/GSVD with much smaller computational complexity, and also gives better classification performance than the other recently proposed ILDA algorithms.
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Sequential Fault Diagnosis Based on LSTM Neural Network
Haitao Zhao,Shaoyuan Sun,Bo Jin +2 more
TL;DR: Extensive experimental results show LSTM can better separate different faults and provide more promising fault diagnosis performance and the novel method can directly classify the raw process data without specific feature extraction and classifier design.
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Supervised optimal locality preserving projection
Wai Keung Wong,Haitao Zhao +1 more
TL;DR: This paper proposes two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal localserving projection (NL-SOL PP), and shows that the proposed SOLPP and NL-SolPP achieve much higher recognition accuracy.
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Rapid and brief communication: Local structure based supervised feature extraction
TL;DR: This paper proposes a novel feature extraction method, called locally discriminating projection (LDP), and compares the proposed LDP approach with LPP, as well as other feature extraction methods, such as PCA and LDA, on the public available data sets, FERET and AR.