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来自专栏杨熹的专栏
What is PCA ?
: A step by step explanation of Principal Component Analysis PCA，Principal Component Analysis, is a dimensionality-reduction
1.2K31发布于 2019-07-24
来自专栏生信菜鸟团
【flowjo】流式笔记：流式也能画t-SNE图？
岂不是能够和单细胞图谱很好地结合互为验证~ tSNE - FlowJo Documentation: https://docs.flowjo.com/flowjo/advanced-features/dimensionality-reduction
1K10编辑于 2024-12-05
来自专栏单细胞天地
单细胞学习之细胞周期分析
modelGeneVarWithSpikes(sce.416b, "ERCC", block=sce.416b$block) chosen.hvgs <- getTopHVGs(dec.416b, prop=0.1) #--- dimensionality-reduction
7.4K43发布于 2020-12-24
来自专栏生信菜鸟团
18 种人类癌症中的基因表达深度剖析 | Nature Biomedical Engineering
- 待补充 Fig. 2: Comparisons of pathway enrichment from DeepProfile with other dimensionality-reduction Comparing DeepProfile to alternative dimensionality-reduction methods 将DeepProfile与替代的降维方法进行比较 Para_01 Comparing DeepProfile pathway coverage to alternative dimensionality-reduction methods 将 DeepProfile
45710编辑于 2025-04-11
来自专栏SnailTyan
Squeeze-and-Excitation Networks论文翻译——中英文对照
mechanism by forming a bottleneck with two fully-connected (FC) layers around the non-linearity, i.e. a dimensionality-reduction
1.6K00发布于 2017-12-28
来自专栏arXiv每日学术速递
机器学习学术速递[11.8]
not directly interpretable, such as when the data is projected onto a two-dimensional space using a dimensionality-reduction
2.1K20发布于 2021-11-17

What is PCA ?

【flowjo】流式笔记：流式也能画t-SNE图？

单细胞学习之细胞周期分析

18 种人类癌症中的基因表达深度剖析 | Nature Biomedical Engineering

Squeeze-and-Excitation Networks论文翻译——中英文对照

机器学习学术速递[11.8]

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