Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset while retaining the important information. In the context of embeddings, dimensionality reduction aims to make the embedding vectors smaller and more efficient without losing critical semantic or structural relationships between data points.
For example, in a high-dimensional embedding space, dimensionality reduction techniques like Principal Component Analysis (PCA), t-SNE, or Autoencoders can help project the embeddings into a lower-dimensional space. This can reduce computational requirements, memory usage, and latency during inference. However, some loss of information might occur if too much dimensionality is reduced.
Dimensionality reduction techniques are often used in conjunction with embeddings to improve their usability in real-world applications, especially when dealing with large datasets or when embedding vectors are too large for efficient storage and processing.