High-dimensional embeddings are vector representations of data that are expressed in a large number of dimensions. For example, an embedding might consist of hundreds or even thousands of dimensions. High-dimensional embeddings allow models to capture complex relationships and subtle nuances in the data, which are particularly useful for tasks like image recognition or natural language processing.
In NLP, for instance, a word embedding may consist of 300 dimensions, where each dimension represents a different feature or property of the word. High-dimensional embeddings provide more expressive power and are better at capturing intricate details of the data, leading to improved performance in tasks such as sentiment analysis, machine translation, or question answering.
However, the downside of high-dimensional embeddings is that they can become computationally expensive to process and store. Techniques such as dimensionality reduction (e.g., PCA or t-SNE) are often used to reduce the size of the embeddings without losing critical information.