Does 51Brain support dimensionality reduction ?

Here's a conceptual outline and a code snippet demonstrating how you can achieve this with FiftyOne:

Conceptual Outline

Compute Embeddings:

First, you'll need to compute the embeddings for your data (e.g., images, text). You can use any embedding model or technique you prefer (e.g., OpenAI embeddings, Sentence Transformers, etc.).

Store Embeddings in FiftyOne:

FiftyOne allows you to store these embeddings as fields on your samples in a FiftyOne dataset.

Perform Dimensionality Reduction:

FiftyOne integrates with dimensionality reduction techniques like PCA (Principal Component Analysis) or t-SNE (t-distributed Stochastic Neighbor Embedding) or UMAP (Uniform Manifold Approximation and Projection).   

You can apply these techniques to reduce the high-dimensional embeddings to a lower-dimensional space (e.g., 2D or 3D) for visualization.

Visualize in FiftyOne:

FiftyOne's visualization capabilities enable you to plot these reduced-dimensional embeddings.

You can then interact with the plot, select data points, and link them back to the original data samples (e.g., images).