"차원축소"의 두 판 사이의 차이

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==관련된 항목들==
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* [[비선형 차원축소]]
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== 노트 ==
 
== 노트 ==
  
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* One of the features in WEKA is a tool for selecting attributes and performing dimensionality reduction.<ref name="ref_05e6">[http://www.turingfinance.com/artificial-intelligence-and-statistics-principal-component-analysis-and-self-organizing-maps/ Dimensionality Reduction Techniques]</ref>
 
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* Dimension reduction of thermistor models for large-area organic light-emitting diodes.<ref name="ref_be72">[https://www.aimsciences.org/article/doi/10.3934/mfc.2019008 Online learning for supervised dimension reduction]</ref>
 
 
* There are multiple techniques that can be used to fight overfitting, but dimensionality reduction is one of the most effective techniques.<ref name="ref_098e">[https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/ Dimensionality Reduction in Python with Scikit-Learn]</ref>
 
* There are multiple techniques that can be used to fight overfitting, but dimensionality reduction is one of the most effective techniques.<ref name="ref_098e">[https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/ Dimensionality Reduction in Python with Scikit-Learn]</ref>
 
* Dimensionality reduction can be used in both supervised and unsupervised learning contexts.<ref name="ref_098e" />
 
* Dimensionality reduction can be used in both supervised and unsupervised learning contexts.<ref name="ref_098e" />
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===소스===
 
===소스===
 
  <references />
 
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==메타데이터==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q16000077 Q16000077]
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===Spacy 패턴 목록===
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* [{'LOWER': 'dimensionality'}, {'LEMMA': 'reduction'}]
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* [{'LOWER': 'dimension'}, {'LEMMA': 'reduction'}]

2021년 2월 17일 (수) 01:39 기준 최신판

관련된 항목들


노트

  • There are multiple techniques that can be used to fight overfitting, but dimensionality reduction is one of the most effective techniques.[1]
  • Dimensionality reduction can be used in both supervised and unsupervised learning contexts.[1]
  • In the case of supervised learning, dimensionality reduction can be used to simplify the features fed into the machine learning classifier.[1]
  • Finally, let's see how LDA can be used to carry out dimensionality reduction.[1]
  • Hence, it is often required to reduce the number of features, which can be done with dimensionality reduction.[2]
  • Dimensionality reduction is the process of reducing the number of variables under consideration.[3]
  • Until recently, linear approaches for dimensionality reduction have been employed.[4]
  • We demonstrate a drastic improvement in dimensionality reduction with the use of nonlinear methods.[4]
  • Therefore, dimensionality reduction refers to the process of mapping an n-dimensional point, into a lower k-dimensional space.[5]
  • Dimensionality reduction may be both linear or non-linear, depending upon the method used.[6]
  • Basically, dimension reduction refers to the process of converting a set of data.[6]
  • There are many methods to perform Dimension reduction.[6]
  • As a result, we have studied Dimensionality Reduction.[6]
  • A comparison of non-linear dimensionality reduction was performed earlier by Romero et al.[7]
  • High-dimensionality statistics and dimensionality reduction techniques are often used for data visualization.[8]
  • Dimensionality reduction is a data preparation technique performed on data prior to modeling.[8]
  • An auto-encoder is a kind of unsupervised neural network that is used for dimensionality reduction and feature discovery.[8]
  • Dimension reduction is the same principal as zipping the data.[9]
  • Dimensionality reduction can help you avoid these problems.[10]
  • We hope that you find this high-level overview of dimensionality reduction helpful.[10]
  • In order to apply the LDA technique for dimensionality reduction, the target column has to be selected first.[11]
  • We implemented all 10 described techniques for dimensionality reduction, applying them to the small dataset of the 2009 KDD Cup corpus.[11]
  • Each one of the 10 parallel lower branches implements one of the described techniques for data-dimensionality reduction.[11]
  • We will perform non-linear dimensionality reduction through Isometric Mapping.[12]
  • We have covered quite a lot of the dimensionality reduction techniques out there.[12]
  • This is as comprehensive an article on dimensionality reduction as you’ll find anywhere![12]
  • Dimensionality reduction is simply, the process of reducing the dimension of your feature set.[13]
  • Avoiding overfitting is a major motivation for performing dimensionality reduction.[13]
  • Popularly used for dimensionality reduction in continuous data, PCA rotates and projects data along the direction of increasing variance.[13]
  • Informally, this is called a Swiss roll, a canonical problem in the field of non-linear dimensionality reduction.[13]

소스

  1. ↑ 이동: 1.0 1.1 1.2 1.3 Dimensionality Reduction in Python with Scikit-Learn
  2. ↑ Introduction to Dimensionality Reduction Technique
  3. ↑ Spark 3.0.1 Documentation
  4. ↑ 이동: 4.0 4.1 Algorithmic dimensionality reduction for molecular structure analysis
  5. ↑ Dimensionality Reduction
  6. ↑ 이동: 6.0 6.1 6.2 6.3 Dimensionality Reduction in Machine Learning
  7. ↑ Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics
  8. ↑ 이동: 8.0 8.1 8.2 Introduction to Dimensionality Reduction for Machine Learning
  9. ↑ What Is Dimension Reduction In Data Science?
  10. ↑ 이동: 10.0 10.1 Dimensionality Reduction: How It Works (In Plain English!)
  11. ↑ 이동: 11.0 11.1 11.2 3 New Techniques for Data-Dimensionality Reduction in Machine Learning – The New Stack
  12. ↑ 이동: 12.0 12.1 12.2 Dimensionality Reduction Techniques
  13. ↑ 이동: 13.0 13.1 13.2 13.3 A beginner’s guide to dimensionality reduction in Machine Learning

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'dimensionality'}, {'LEMMA': 'reduction'}]
  • [{'LOWER': 'dimension'}, {'LEMMA': 'reduction'}]
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