类不是集群:改进基于标签的尺寸缩小评估

相关实验视频

Updated: Jul 11, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Hyeon Jeon, Yun-Hsin Kuo, Michael Aupetit

    IEEE transactions on visualization and computer graphics
    |November 3, 2023
    PubMed
    概括

    新的标签-可靠性和标签-连续性 (标签-T&C) 措施通过评估集群保存来准确评估缩小维度 (DR) 嵌入,而不是假设先前存在的类分离. 这些方法改善了DR可靠性评估.

    更多相关视频

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations12:27

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

    Published on: February 15, 2017

    7.0K
    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data04:57

    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

    Published on: May 16, 2022

    15.9K

    相关概念视频

    Routh-Hurwitz Criterion II01:19

    Routh-Hurwitz Criterion II

    In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
    The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
    260
    Ordinal Level of Measurement00:55

    Ordinal Level of Measurement

    The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
    Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
    24.3K
    Improving Translational Accuracy02:07

    Improving Translational Accuracy

    Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
    11.3K
    Routh-Hurwitz Criterion I01:15

    Routh-Hurwitz Criterion I

    Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
    To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
    254
    Quantifying and Rejecting Outliers: The Grubbs Test01:02

    Quantifying and Rejecting Outliers: The Grubbs Test

    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...
    1.6K
    Labeling Emotion01:20

    Labeling Emotion

    Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
    145