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## 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

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

## 메타데이터

### 위키데이터

- ID : Q16000077