# 차원축소

둘러보기로 가기 검색하러 가기

## 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]
• 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]

## 메타데이터

### Spacy 패턴 목록

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