Yongshan Zhang
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View article: Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering
Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering Open
Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face seve…
View article: Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering
Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering Open
Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face seve…
View article: Lossless Compression Framework Using Lossy Prior for High-Resolution Remote Sensing Images
Lossless Compression Framework Using Lossy Prior for High-Resolution Remote Sensing Images Open
Lossless compression of remote sensing images is critically important for minimizing storage requirements while preserving the complete integrity of the data. The main challenge in lossless compression lies in striking a good balance betwe…
View article: Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification
Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification Open
Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predictive accuracy in this field, they lack the ability to rigoro…
View article: Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk Assessment
Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk Assessment Open
The significance of financial risk lies in its impact on economic stability and individual/institutional financial security. Effective risk management is crucial for market confidence and crisis prevention. Current methods for multivariate…
View article: Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image Compression
Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image Compression Open
Lossless remote sensing image compression aims to reduce the storage size of images without any information loss, ensuring that the decompressed image is identical to the original one. Most existing methods focus on lossy image compression…
View article: Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification Open
Identifying the land cover category for each pixel in a hyperspectral image (HSI) relies on spectral and spatial information. An HSI cuboid with a specific patch size is utilized to extract spatial-spectral feature representation for the c…
View article: Tensor-Based Unsupervised Multi-View Feature Selection for Image Recognition
Tensor-Based Unsupervised Multi-View Feature Selection for Image Recognition Open
In image analysis, image samples from multiple sources may contain noisy features. Due to the difficulty of obtaining label information and complex intrinsic structures, performing unsupervised feature selection on multi-view data is a cha…
View article: Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine
Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine Open
Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental in…
View article: Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery
Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery Open
Dimensionality Reduction (DR) models are of significance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph…