Dimensionality reduction is a key technique in machine learning and data mining. It is used to reduce the number of input variables to a dataset, which can help improve model performance, reduce computational complexity, and facilitate data visualization. In this article, we will discuss some common dimensionality reduction techniques.

Common Dimensionality Reduction Techniques

  1. Principal Component Analysis (PCA) PCA is a linear dimensionality reduction technique that transforms the original variables into a new set of variables, the principal components, which are uncorrelated and capture the maximum variance in the data.

    PCA Diagram

  2. Linear Discriminant Analysis (LDA) LDA is a supervised dimensionality reduction technique that finds a linear combination of features that best separates different classes in the dataset.

  3. Non-negative Matrix Factorization (NMF) NMF is an unsupervised dimensionality reduction technique that factorizes a non-negative matrix into two non-negative matrices, which represent the feature representation and the feature coefficients.

  4. Autoencoders Autoencoders are neural networks that learn to compress and then decompress data. They can be used for dimensionality reduction by setting the number of hidden units to be less than the input size.

  5. t-SNE and UMAP t-SNE and UMAP are non-linear dimensionality reduction techniques that are particularly useful for visualizing high-dimensional data in two or three dimensions.

References

For more information on dimensionality reduction techniques, you can read our detailed guide on Dimensionality Reduction Techniques.

This article provides a brief overview of dimensionality reduction techniques. If you have any questions or comments, please feel free to reach out to us.