来源:Deephub Imba 本文约2000字,建议阅读4分钟
本文介绍了今年5篇关于降维方法的论文。
1、Dimension Reduction for Spatially Correlated Data: Spatial Predictor Envelope
2、Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra
清理和验证数据; 基于汇总统计(位置和可变性的估计)的初始探索性数据分析; 探索和量化数据中现有的相关性; 预处理和线性变换数据到它的主要成分; 降维和流形学习; 聚类和异常检测; 数据的可视化和解释。
3、Statistical Treatment, Fourier and Modal Decomposition
4、SLISEMAP: Explainable Dimensionality Reduction
5、A comprehensive survey on computational learning methods for analysis of gene expression data in genomics
引用:
Dimension Reduction for Spatially Correlated Data: Spatial Predictor Envelope https://arxiv.org/pdf/2201.01919.pdf
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra https://arxiv.org/pdf/2201.02696.pdf
Statistical Treatment, Fourier and Modal Decomposition https://arxiv.org/pdf/2201.03847.pdf
SLISEMAP: Explainable Dimensionality Reduction https://arxiv.org/pdf/2201.04455.pdf
A comprehensive survey on computational learning methods for analysis of gene expression data in genomics https://arxiv.org/pdf/2202.02958.pdf