Data Preprocessing Questions
The common techniques used for dimensionality reduction are:
1. Principal Component Analysis (PCA): It is a statistical technique that transforms a high-dimensional dataset into a lower-dimensional space by identifying the most important features or principal components that explain the maximum variance in the data.
2. Linear Discriminant Analysis (LDA): LDA is a supervised dimensionality reduction technique that aims to find a linear combination of features that maximizes the separation between different classes or categories in the data.
3. t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a nonlinear dimensionality reduction technique that is particularly useful for visualizing high-dimensional data in a lower-dimensional space. It preserves the local structure of the data and is often used for exploratory data analysis.
4. Autoencoders: Autoencoders are neural network models that are trained to reconstruct the input data from a compressed representation. The compressed representation, also known as the bottleneck layer, acts as a lower-dimensional representation of the original data.
5. Feature selection: Feature selection techniques aim to select a subset of the most relevant features from the original dataset. This can be done using statistical methods, such as correlation analysis or mutual information, or through algorithms like Recursive Feature Elimination (RFE) or LASSO.
6. Non-negative Matrix Factorization (NMF): NMF is a dimensionality reduction technique that decomposes a non-negative matrix into two lower-rank matrices. It is particularly useful for analyzing non-negative data, such as text or image data.
These techniques help in reducing the dimensionality of the data, which can improve computational efficiency, reduce noise, and enhance the interpretability of the data.