Dimensionality Reduction
- (Harvard University - Harvard Taiwan Student Association)
- Overview
Dimensionality reduction is a statistical tool that reduces the number of variables in a training dataset. The goal is to reduce the complexity of the dataset while retaining the most important properties of the original data.
Here are some dimensionality reduction techniques:
- Principal Component Analysis (PCA): A popular algorithm for dimensionality reduction
- Linear Discriminant Analysis (LDA): A predictive modeling algorithm for multi-class classification. LDA can also be used as a dimensionality reduction technique
- Factor analysis: An unsupervised machine learning algorithm that creates factors from observed variables to represent the common variance. Factor analysis is an extension of PCA, but its main focus is on finding latent variables
- Backward feature elimination: Starts with all the features and removes the least significant feature at each iteration
- Low variance filter: Calculates each column variance and removes those columns with a variance value below a given threshold. This method only applies to numerical columns
[More to come ...]
