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Related Concept Videos

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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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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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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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Related Experiment Videos

Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation.

Adin Ramirez Rivera, Adil Khan, Imad Eddine Ibrahim Bekkouch

    IEEE Transactions on Neural Networks and Learning Systems
    |October 16, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel hierarchical latent space for generating synthetic anomalies, crucial for imbalanced data in anomaly detection. This method enables robust zero-shot outlier generation and classifier training without needing real outlier examples.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Anomaly detection is challenged by highly imbalanced datasets where anomalies are rare.
    • Existing synthetic data generation methods require expressive representations for high-quality anomaly synthesis.
    • Zero-shot anomaly generation necessitates robust feature descriptors and generative models.

    Purpose of the Study:

    • To propose a two-level hierarchical latent space representation for robust feature distillation.
    • To enable zero-shot anomaly generation and improve outlier detection performance.
    • To train effective binary classifiers using synthetically generated negative samples.

    Main Methods:

    • Utilized autoencoders (AEs) for initial feature descriptor distillation.
    • Employed variational autoencoders (VAEs) to create robust representations from a variational family of distributions.
    • Developed a hierarchical structure for feature distillation and fusion.
    • Selected latent distributions on the outskirts of training data for synthetic-outlier generation.

    Main Results:

    • The hierarchical structure yields robust and generalizable representations.
    • Successfully synthesized pseudo outlier samples without prior exposure to real outliers.
    • Trained effective binary classifiers for true outlier detection using synthetic data.
    • Demonstrated strong performance on multiple anomaly detection benchmarks.

    Conclusions:

    • The proposed hierarchical latent space effectively addresses the challenge of imbalanced data in anomaly detection.
    • Zero-shot anomaly generation is feasible and beneficial for training robust classifiers.
    • This approach offers a viable solution for scenarios lacking sufficient or defined outlier data.