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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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Standard Entropy Change for a Reaction03:00

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Entropy and Solvation02:05

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Entropy within the Cell01:22

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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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Entropy and the Second Law of Thermodynamics01:20

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
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Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
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Kernel Entropy Component Analysis with Nongreedy L1-Norm Maximization.

Haijin Ji1,2, Song Huang1

  • 1Command & Control Engineering College, Army Engineering University of PLA, Nanjing 210007, China.

Computational Intelligence and Neuroscience
|November 9, 2018
PubMed
Summary
This summary is machine-generated.

A new method, KECA-L1, enhances dimensionality reduction by using L1-norm for robustness against outliers and improved computational efficiency over OKECA. This approach shows superior performance in data classification and software defect prediction tasks.

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Area of Science:

  • Machine Learning
  • Data Science
  • Pattern Recognition

Background:

  • Kernel Entropy Component Analysis (KECA) is an advanced dimensionality reduction (DR) technique.
  • Optimized KECA (OKECA) offers improved expressive power but suffers from outlier sensitivity and high computational cost due to L2-norm.
  • Existing DR methods like Principal Component Analysis (PCA) have limitations in certain pattern analysis applications.

Purpose of the Study:

  • To develop a novel, robust, and computationally efficient DR method based on KECA.
  • To address the limitations of OKECA, specifically its sensitivity to outliers and high complexity.
  • To enhance feature extraction capabilities using L1-norm for improved information retention.

Main Methods:

  • Introduction of KECA-L1, a new extension of KECA utilizing L1-norm for a more robust kernel decomposition matrix.
  • Development of a non-greedy iterative algorithm for faster convergence compared to OKECA.
  • Integration of a general semi-supervised classifier for KECA-based methods to evaluate classification performance.

Main Results:

  • KECA-L1 demonstrates superior robustness against outliers compared to OKECA.
  • The proposed non-greedy iterative algorithm offers significantly faster convergence.
  • Extensive experiments confirm KECA-L1's superiority over existing KECA and PCA-based methods in data classification and software defect prediction.

Conclusions:

  • KECA-L1 provides a robust and efficient alternative for dimensionality reduction and feature extraction.
  • The L1-norm based approach effectively mitigates outlier sensitivity and computational complexity.
  • The developed method shows significant promise for applications in data classification and software defect prediction, with publicly available code.