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相关概念视频

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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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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Fischer Projections02:18

Fischer Projections

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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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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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联合结构化二分位图和排列间隔投影用于大规模特征选择.

Xia Dong, Feiping Nie, Danyang Wu

    IEEE transactions on neural networks and learning systems
    |May 8, 2024
    PubMed
    概括

    这项研究引入了一种新的大规模特征选择方法RS2BLFS,该方法集成了结构化的双部分图形与行间隔投影,以实现最佳的,无监督的特征子集选择. 它在聚类非球形数据方面表现出色,优于传统方法.

    科学领域:

    • 机器学习 机器学习
    • 数据挖掘 数据挖掘
    • 计算统计学 计算统计学

    背景情况:

    • 传统的基于图形的特征选择方法由于其两阶段的独立处理,通常会产生低于最佳的结果.
    • 现有的技术与大规模的非球形数据集作斗争,限制了它们在复杂数据分析中的适用性.

    研究的目的:

    • 为大规模特征选择和聚类提出一种全新的,综合的方法.
    • 通过共同优化图形构造和特征表示,提高非球形数据集特征选择的性能.

    主要方法:

    • 介绍了带有结构双分线图 (RS2BLFS) 的排列间隔投影,将图形构造与排列间隔投影学习相结合.
    • 开发了一种理论分析的算法,以解决无监督特征选择的集成优化问题.
    • 利用双部分图的结构,连接的组件代表集群和子集群,以改进数据表示.

    主要成果:

    • RS2BLFS有效地以无监督的方式进行联合特征选择和集群.
    • 与传统方法相比,该方法在合成和现实世界数据集上表现出卓越的性能.
    • 综合方法成功处理大规模的非球形数据,提高了特征选择准确度.

    结论:

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  • RS2BLFS在大规模的特征选择和集群方面取得了重大进展,特别是在复杂的数据结构中.
  • 基于图形和投影的集成学习方法提供了一个比顺序方法更优越和更有效的解决方案.
  • 拟议的技术非常有效地揭示了潜在的集群结构,并同时选择相关特征.