論文閱讀：Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection
Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection 本文主要乾了三件事： 1、首先利用權值向量v對t時刻不同樣本的重構誤差進行線性組合。 2、基於圖的正則化矩陣tS，用於從保持局部流形結構的角度檢查所選波段的代表性。 3、使用緩存矢量q去捕獲樣本矩陣之間的一致性並執行頻帶選擇。 創新點： 1
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相關文章
- 1. Hyperspectral Band Selection by Multitask Sparsity Puisuit
- 2. 論文解讀：BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
- 3. node2vec: Scalable Feature Learning for Networks論文解讀
- 4. [論文解讀]Test Selection for Deep Learning Systems
- 5. 《論文閱讀》Grid-GCN for Fast and Scalable Point Cloud Learning
- 6. [論文閱讀筆記] node2vec Scalable Feature Learning for Networks
- 7. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 8. Variable precision rough set based unsupervised band selection technique for hyperspectral image cla
- 9. node2vec: Scalable Feature Learning for Networks閱讀報告
- 10. 調程序感悟：Group lasso-based band selection for hyperspectral image classification