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Linearization and Approximation01:26

Linearization and Approximation

119
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
119
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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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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Application of Linearization and Approximation01:29

Application of Linearization and Approximation

127
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
127
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

641
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...
641
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

554
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
554
Second Uniqueness Theorem01:16

Second Uniqueness Theorem

2.7K
Consider a region consisting of several individual conductors with a definite charge density in the region between these conductors. The second uniqueness theorem states that if the total charge on each conductor and the charge density in the in-between region are known, then the electric field can be uniquely determined.
In contrast, consider that the electric field is non-unique and apply Gauss's law in divergence form in the region between the conductors and the integral form to the surface...
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Related Experiment Video

Updated: Mar 9, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Pairwise Identity Verification via Linear Concentrative Metric Learning.

Lilei Zheng, Stefan Duffner, Khalid Idrissi

    IEEE Transactions on Cybernetics
    |December 29, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Metric learning for identity verification, like face and speaker recognition, is challenging with limited data. A simple linear model trained on similar pairs offers competitive performance, especially with few training examples.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Pattern Recognition

    Background:

    • Pairwise identity verification tasks, such as face and speaker recognition, face challenges due to exclusive training and testing individuals and limited training data.
    • Existing metric learning systems require robust optimization strategies to handle these constraints.

    Purpose of the Study:

    • To present a general framework for metric learning systems applied to pairwise identity verification.
    • To investigate the effectiveness of different metric learning models (linear, nonlinear, similarity, distance) under restricted and unrestricted training settings.

    Main Methods:

    • Developed a general framework for metric learning systems.
    • Employed the stochastic gradient descent algorithm for optimization.
    • Evaluated linear and shallow nonlinear models for both similarity and distance metric learning.

    Main Results:

    • Learning a linear system on similar pairs only is preferable with limited training data, offering simplicity and competitive performance.
    • The proposed approach achieved competitive results on the Labeled Faces in the Wild (LFW) face dataset and the NIST speaker dataset.
    • A pretrained deep nonlinear model significantly improved face verification results.

    Conclusions:

    • For pairwise verification with limited data, a linear metric learning system trained on similar pairs is a practical and effective solution.
    • Deep nonlinear models can enhance performance, particularly in face verification tasks.
    • The study provides valuable insights into optimizing metric learning for identity verification systems.