차원축소

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  • One of the features in WEKA is a tool for selecting attributes and performing dimensionality reduction.[1]
  • Dimension reduction of thermistor models for large-area organic light-emitting diodes.[2]
  • There are multiple techniques that can be used to fight overfitting, but dimensionality reduction is one of the most effective techniques.[3]
  • Dimensionality reduction can be used in both supervised and unsupervised learning contexts.[3]
  • In the case of supervised learning, dimensionality reduction can be used to simplify the features fed into the machine learning classifier.[3]
  • Finally, let's see how LDA can be used to carry out dimensionality reduction.[3]
  • Hence, it is often required to reduce the number of features, which can be done with dimensionality reduction.[4]
  • Dimensionality reduction is the process of reducing the number of variables under consideration.[5]
  • Until recently, linear approaches for dimensionality reduction have been employed.[6]
  • We demonstrate a drastic improvement in dimensionality reduction with the use of nonlinear methods.[6]
  • Therefore, dimensionality reduction refers to the process of mapping an n-dimensional point, into a lower k-dimensional space.[7]
  • Dimensionality reduction may be both linear or non-linear, depending upon the method used.[8]
  • Basically, dimension reduction refers to the process of converting a set of data.[8]
  • There are many methods to perform Dimension reduction.[8]
  • As a result, we have studied Dimensionality Reduction.[8]
  • A comparison of non-linear dimensionality reduction was performed earlier by Romero et al.[9]
  • High-dimensionality statistics and dimensionality reduction techniques are often used for data visualization.[10]
  • Dimensionality reduction is a data preparation technique performed on data prior to modeling.[10]
  • An auto-encoder is a kind of unsupervised neural network that is used for dimensionality reduction and feature discovery.[10]
  • Dimension reduction is the same principal as zipping the data.[11]
  • Dimensionality reduction can help you avoid these problems.[12]
  • We hope that you find this high-level overview of dimensionality reduction helpful.[12]
  • In order to apply the LDA technique for dimensionality reduction, the target column has to be selected first.[13]
  • We implemented all 10 described techniques for dimensionality reduction, applying them to the small dataset of the 2009 KDD Cup corpus.[13]
  • Each one of the 10 parallel lower branches implements one of the described techniques for data-dimensionality reduction.[13]
  • We will perform non-linear dimensionality reduction through Isometric Mapping.[14]
  • We have covered quite a lot of the dimensionality reduction techniques out there.[14]
  • This is as comprehensive an article on dimensionality reduction as you’ll find anywhere![14]
  • Dimensionality reduction is simply, the process of reducing the dimension of your feature set.[15]
  • Avoiding overfitting is a major motivation for performing dimensionality reduction.[15]
  • Popularly used for dimensionality reduction in continuous data, PCA rotates and projects data along the direction of increasing variance.[15]
  • Informally, this is called a Swiss roll, a canonical problem in the field of non-linear dimensionality reduction.[15]

소스

  1. ↑ Dimensionality Reduction Techniques
  2. ↑ Online learning for supervised dimension reduction
  3. ↑ 3.0 3.1 3.2 3.3 Dimensionality Reduction in Python with Scikit-Learn
  4. ↑ Introduction to Dimensionality Reduction Technique
  5. ↑ Spark 3.0.1 Documentation
  6. ↑ 6.0 6.1 Algorithmic dimensionality reduction for molecular structure analysis
  7. ↑ Dimensionality Reduction
  8. ↑ 8.0 8.1 8.2 8.3 Dimensionality Reduction in Machine Learning
  9. ↑ Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics
  10. ↑ 10.0 10.1 10.2 Introduction to Dimensionality Reduction for Machine Learning
  11. ↑ What Is Dimension Reduction In Data Science?
  12. ↑ 12.0 12.1 Dimensionality Reduction: How It Works (In Plain English!)
  13. ↑ 13.0 13.1 13.2 3 New Techniques for Data-Dimensionality Reduction in Machine Learning – The New Stack
  14. ↑ 14.0 14.1 14.2 Dimensionality Reduction Techniques
  15. ↑ 15.0 15.1 15.2 15.3 A beginner’s guide to dimensionality reduction in Machine Learning
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