D
Delin Chu
Researcher at National University of Singapore
Publications - 105
Citations - 1864
Delin Chu is an academic researcher from National University of Singapore. The author has contributed to research in topics: Matrix (mathematics) & Eigenvalues and eigenvectors. The author has an hindex of 24, co-authored 102 publications receiving 1651 citations. Previous affiliations of Delin Chu include Tsinghua University & Beihang University.
Papers
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Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition
TL;DR: A dimensionality reduction method that fits SRC well, which maximizes the ratio of between- class reconstruction residual to within-class reconstruction residual in the projected space and thus enables SRC to achieve better performance.
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Sparse Canonical Correlation Analysis: New Formulation and Algorithm
TL;DR: This paper studies canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables, and proposes a new sparse CCA algorithm to solve the multiple CCA problem.
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Regularization of Singular Systems by Derivative and Proportional Output Feedback
TL;DR: In this paper, the problem of the regularization of singular systems by derivative and proportional output feedback is studied, and necessary and sufficient conditions are given to guarantee the existence of a derivative and output feedback such that the closed-loop system is regular and of index at most 1.
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Necessary and sufficient conditions for the output feedback regularization of descriptor systems
Delin Chu,Daniel W. C. Ho +1 more
TL;DR: The problem of output feedback regularization of descriptor systems is studied and necessary and sufficient conditions are given to regulate the descriptor systems by derivative and proportional output feedback into a closed-loop regular system.
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Incremental Linear Discriminant Analysis: A Fast Algorithm and Comparisons
TL;DR: Numerical experiments demonstrate that the proposed new batch LDA algorithm called LDA/QR is very efficient and competitive with the state-of-the-art ILDA algorithms in terms of classification accuracy, computational complexity, and space complexity.