2024年4月8日 · 5. Sparse Transformer Sparse Transformer 是一种专为处理大规模序列数据设计的模型，它通过采用稀疏注意力机制来降低计算复杂度，同时保持模型性能。 这种模型特别适用于处理那些传统Transformer模型因序列长度过长而难以处理的任务。

In mathematics, particularly matrix theory, a band matrix or banded matrix is a sparse matrix whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.

Hyperspectral images consist of large number of spectral bands but many of which contain redundant information. Therefore, band selection has been a common prac

2019年8月15日 · In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands.

2024年3月15日 · Band attention and sparse constraint (SC) maximizes the removal of redundant bands. A new loss function is defined to solve the gradient update problem caused by SC. The selected bands carry important spectrochemical information. This method is also applicable to other hyperspectral data of food and agro-products.

2020年2月4日 · Abstract: Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed.

A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented.

In this context, this article proposes the structure-preserved and weakly redundant (SPWR) band selection method for hyperspectral imagery (HSI). Initially, to preserve the spatial structure of HSI, heterogeneous regions are generated by superpixel segmentation.

To address these issues, this article proposes a novel spectral-spatial multi-view sparse self-representation model for hyperspectral BS. Firstly, a dynamic grouping strategy is designed to partition bands by incorporating the local and global adjacencies, promoting intra-group coherence and inter-group disparity.

2020年4月28日 · In this chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands.