Dingyi Wang
9 Papers
Dingyi Wang is an academic researcher. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has co-authored 3 publications.
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Papers
Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining
Lifeng Yin,Lei Lv,Dingyi Wang,Yingwei Qu,Huayue Chen,Wu Deng +5 more
TL;DR: A spectral clustering method using k-means and weighted Mahalanobis distance (Referred to as MDLSC) to enhance the degree of correlation between data points and improve the clustering accuracy of Laplacian matrix eigenvectors, which maximizes the retention of the distribution characteristics of the original data.
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Compressive Hyperspectral Target Detection with Restricted Distribution Property
Qingyao Yang,Xiaoqin Wang,Dingyi Wang,Baoying Yu,Yumei Zhou,Shushan Qiao +5 more
TL;DR: Compressive hyperspectral target detection without reconstruction based on restricted distribution property. The proposed algorithm utilizes the restricted distribution property of spectral vectors in compressed hyperspectral data to achieve comparable or superior performance to conventional methods in original hyperspectral images.
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Self-eliminating Discriminant Analysis Dictionary Learning for Pattern Classification
TL;DR: This work designs a novel analysis dictionary regularization term to improve the discrimination capability of analysis dictionary by eliminating repeated and linearly dependent atoms in the analysis dictionary while preventing the generation of trivial solutions and introduces a linear classification error term into SeDADL model to learn a linear classifier.
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A Low-Power and Low-Latency Speech Feature Extractor Based on Time-Domain Filter Bank
TL;DR: A low-power and low-latency speech feature extractor based on time-domain filter bank significantly reduces power consumption and latency compared to conventional systems.
1
Sinc‐attention feature extraction for trivial‐event based speaker verification
TL;DR: In this article , a Sinc-Attention feature extraction method is proposed to extract more discriminative speech features from speech signals to achieve a robust speaker verification (SV) system for trivial events.
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