github-actions[bot]
commented
2 months ago Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!
Is there an existing issue for this?
Feature Description
Dimensionality reduction techniques are methods used to reduce the number of input variables in a dataset while retaining as much information as possible. These techniques transform high-dimensional data into a lower-dimensional form, making it easier to visualize, interpret, and process. Common techniques include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA).
Use Case
Incorporating dimensionality reduction techniques into the project would enhance data preprocessing and visualization capabilities. It would allow users to efficiently reduce the complexity of their data, identify and interpret patterns, and improve the performance of machine learning models by eliminating redundant features. This is particularly useful in fields like image processing, genomics, and finance, where high-dimensional data is prevalent.
Benefits
No response
Add ScreenShots
No response
Priority
High
Record