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  • 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]

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