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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Sampling Plans01:23

Sampling Plans

219
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
219
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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相关实验视频

Updated: Jul 27, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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一个没有超参数的,快速和高效的框架来检测基于超高维特征的有限样本中的集群.

Shahina Rahman1, Valen E Johnson1, Suhasini Subba Rao1

  • 1Department of Statistics, Texas A & M University, College Station, TX 77843, USA.

IEEE access : practical innovations, open solutions
|June 5, 2023
PubMed
概括

这项研究引入了一种新的机器学习聚类方法,用于高维,小样本数据. 该算法在没有事先参数规范的情况下准确地识别出未知的集群组,在速度和准确性方面超过现有方法.

科学领域:

  • 计算生物学 计算生物学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 聚类涉及将N个对象分组为K0个组,使用P的特征.
  • 高维,小样本大小 (N ≪ P) 聚类在生物学,医学和社会科学中至关重要.
  • 现有的方法通常需要知道集群的数量 (K0) 或调参数,这在无监督学习中是有问题的.

研究的目的:

  • 开发一种新的集群算法,用于高维,小样本设置,其中集群数量 (K0) 是未知的.
  • 克服现有方法的局限性,这些方法需要先前了解K0或调参数.
  • 为无监督学习问题提供准确有效的集群解决方案.

主要方法:

  • 该方法采用格拉姆矩阵的转换.
  • 它将强大的大数定律应用于转换矩阵.
  • 显示特征向量在衰变特征相关性下集中在一个低维空间.

主要成果:

  • 该算法有效地检测和可视化未知的集群配置.
  • 在32个微阵列数据集上进行了测试,与其他21种集群方法相比,它表现出了优异的性能.
  • 拟议的算法在确定最佳集群配置方面是两倍准确和更快的.
关键词:
集群集成是指集群集成.基因组矩阵矩阵是什么高维特征是指高维特征的特征.没有超参数的自由超参数.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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相关实验视频

Last Updated: Jul 27, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

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结论:

  • 这种新的集群方法为高维,小样本数据分析提供了强大的解决方案.
  • 它消除了预先指定集群数量或调参数的需求.
  • 该算法的效率和准确性使其对生物信息学和其他科学领域的应用非常有价值.