11 Papers
Lin Li is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 1, co-authored 1 publications.
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Papers
Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction
TL;DR: A novel dimensionality reduction method called supervised discriminant Isomap is proposed to solve the first two problems mentioned above and can capture more discriminative information from raw data than other isomap based methods.
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Effects of climate change on vegetation dynamics of the Qinghai-Tibet Plateau, a causality analysis using empirical dynamic modeling
TL;DR: In this article , the authors quantify causal effects of climate factors on vegetation dynamics with an empirical dynamical model (EDM) -a nonlinear dynamical systems analysis approach based on state-space reconstruction rather than correlation.
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Robust discriminative projection with dynamic graph regularization for feature extraction and classification
TL;DR: Wang et al. as mentioned in this paper proposed robust discriminative projection with dynamic graph regularization (RDPDG) to deal with the problem of noisy and outlier mixed data.
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A deep hypersphere approach to high-dimensional anomaly detection
TL;DR: In this paper , the authors proposed a detection method using the combination of an autoencoder and a hypersphere, where an angle kernel and a radius kernel are derived in order to learn a compact boundary of distinguishing anomalous and normal instances.
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Unsupervised dimensionality reduction by jointing dynamic hypergraph and low-rank embedding for classification and clustering
TL;DR: Joint dynamic hypergraph and low-rank embedding (DHLRE) as mentioned in this paper unifies hypergraph learning and low rank learning into a single objective function, and the weight of hyperedge and hypergraph are dynamically generated in dimension-reduced space, which suppresses the ability of noise or outlier to affect the projection matrix.
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