Dimensionality Reduction Algorithms Algorithm
Dimensionality Reduction Algorithms are a class of machine learning techniques that aim to reduce the number of features in a dataset while preserving its essential structure and relationships. These algorithms are primarily used for data preprocessing, visualization, and noise reduction, as well as improving computational efficiency and mitigating the curse of dimensionality. High-dimensional data can be challenging to analyze and interpret, as it may contain irrelevant or redundant features, and may lead to overfitting in machine learning models. Dimensionality reduction algorithms help to address these issues by transforming the original high-dimensional data into a lower-dimensional space, thereby retaining only the most important information and discarding the rest.
Two of the most widely used dimensionality reduction algorithms are Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). PCA is a linear technique that computes the principal components of the data, which are linear combinations of the original features that capture the maximum variance in the dataset. These principal components can then be used as new features for further analysis or modeling. On the other hand, t-SNE is a non-linear technique that aims to preserve local relationships between data points by minimizing the divergence between probability distributions in the high-dimensional and low-dimensional spaces. This makes t-SNE particularly useful for visualizing complex data structures, as it can capture non-linear patterns and relationships. Other popular dimensionality reduction algorithms include Linear Discriminant Analysis (LDA), Autoencoders, and Uniform Manifold Approximation and Projection (UMAP), each having its unique strengths and limitations depending on the specific application or dataset.
library(stats)
pca <- princomp(train, cor = TRUE)
train_reduced <- predict(pca,train)
test_reduced <- predict(pca,test)
