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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

30.4K
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...
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Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Nominal Level of Measurement00:56

Nominal Level of Measurement

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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. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
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McNemar's Test01:23

McNemar's Test

690
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

752
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...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Supervised Categorical Metric Learning With Schatten p-Norms.

Yaqiong Li, Xuhui Fan, Eric Gaussier

    IEEE Transactions on Cybernetics
    |July 23, 2020
    PubMed
    Summary

    Categorical projected metric learning (CPML) offers efficient and accurate methods for categorical data. This approach improves prediction accuracy and reduces computational time for metric learning tasks.

    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Science

    Background:

    • Metric learning excels with numerical data but lags in categorical data applications.
    • Existing methods for categorical data lack efficiency and optimal prediction accuracy.

    Purpose of the Study:

    • To introduce Categorical Projected Metric Learning (CPML), an efficient method for categorical data.
    • To enhance prediction accuracy and reduce computational time in metric learning for categorical datasets.

    Main Methods:

    • Utilizing a value distance metric for data representation.
    • Developing novel distance metrics tailored for categorical data.
    • Generalizing regularizers via the Schatten p-norm and providing generalization bounds.

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    Main Results:

    • CPML achieves state-of-the-art prediction accuracy on categorical data.
    • The proposed method significantly reduces computational time compared to existing approaches.
    • A new generalization bound complements existing metric learning bounds.

    Conclusions:

    • CPML effectively addresses the challenges of metric learning in categorical data.
    • The method offers a computationally efficient and highly accurate solution.
    • The generalization bound contributes theoretical insights to metric learning research.