Yes, embeddings can be visualized, especially when their dimensionality is reduced to two or three dimensions. Visualization of embeddings is useful for understanding the relationships between different data points in the embedding space. One common method for visualization is using dimensionality reduction techniques like t-SNE (t-distributed Stochastic Neighbor Embedding) or PCA (Principal Component Analysis), which reduce the high-dimensional embeddings to a lower-dimensional space for easy visualization.
For instance, in the case of word embeddings, t-SNE can help visualize how words with similar meanings are grouped together in the 2D space, revealing clusters of related words. Similarly, images or documents can be represented in a 2D or 3D scatter plot, where the proximity of points indicates the similarity between them. This type of visualization can help identify patterns, clusters, and outliers within the data.
While dimensionality reduction techniques can provide insights into the structure of embeddings, they are not always perfect and may distort some relationships, especially in cases where the embeddings have very high dimensions. However, they remain powerful tools for exploratory analysis and for visually interpreting the semantic properties of embeddings.