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相关概念视频

What Are Outliers?01:12

What Are Outliers?

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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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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Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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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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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
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通过稀缺特征表示来低拍无监督的视觉异常检测.

Fanghui Zhang, Haiyue Zhu, Yigang Cen

    IEEE transactions on neural networks and learning systems
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    本研究引入了一种用于工业制造的新型稀疏特征表示异常检测 (SFRAD) 框架. 在低射门场景中,SFRAD增强了概括性,在无监督异常检测中超过了现有的方法.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 工业制造业 工业制造业 工业制造业

    背景情况:

    • 视觉异常检测对于工业制造质量控制至关重要.
    • 基于对相似距离的现有方法在概括上有局限性,尤其是有限的数据.
    • 绝对相似的距离难以将比较扩展到可用样本之外,阻碍了低射线场景中的性能.

    研究的目的:

    • 提出一个新的稀疏特征表示异常检测 (SFRAD) 框架.
    • 解决异常检测中的一般化挑战,特别是在低射线场景中.
    • 建议使用直角匹配追踪 (ASOMP) 提出一种新的异常得分指标.

    主要方法:

    • 制定了异常检测作为一个稀疏的特征表示问题.
    • 采用直角匹配追求 (OMP) 算法用于稀疏表示.
    • 引入了一个基本特征采样 (BFS) 算法,用于高效的内存库构建,平衡覆盖和表示.

    主要成果:

    • 在多个基准数据集 (MVTec AD,KolektorSDD,MNIST,CIFAR-10等) 中,SFRAD框架表现出卓越的性能. ) 的情况.
    • 在无监督异常检测方面取得了最先进的结果.
    • 在低射程异常检测场景中显著提高性能.

    结论:

    • SFRAD有效地结合了绝对相似性和线性表示的优势.
    • 拟议的框架增强了概括能力,特别是对于有限的样本大小.
    • 在无监督和低射击视觉异常检测方面,SFRAD代表了重大进步.