Embeddings and one-hot encoding are two different methods used for representing categorical data in machine learning and natural language processing. One-hot encoding creates a binary vector for each unique category, where each vector has a length equal to the number of unique categories. In this representation, only one element is '1' (indicating the presence of that category), and all others are '0'. For instance, if you have three categories: "cat," "dog," and "bird," the one-hot encoding would look like this: "cat" is represented as [1, 0, 0], "dog" as [0, 1, 0], and "bird" as [0, 0, 1]. This approach is simple and useful for smaller datasets but can lead to high-dimensional vectors when dealing with many categories, making it inefficient in terms of performance and storage.
In contrast, embeddings transform categorical data into dense vectors of fixed size, often of much lower dimensionality than one-hot encoding. These vectors capture more semantic relationships and patterns between the categories because similar categories can have similar embeddings. For example, in word embeddings such as Word2Vec, words that are used in similar contexts will have closer vector representations in the embedding space. If we took our earlier example and used embeddings, "cat," "dog," and "bird" might be represented as [0.2, 0.3], [0.1, 0.4], and [0.5, 0.6] in a 2-dimensional space. This not only reduces the size of the representation but also creates a way to understand and analyze relationships between categories.
The choice between embeddings and one-hot encoding often depends on the specific problem and dataset size. One-hot encoding works well for small, distinct categories, typical in simpler problems or when the relationships between categories do not matter much. On the other hand, embeddings are preferable for larger datasets with many categories or when there are inherent relationships among the categories, such as words in natural language processing or items in a recommendation system. In summary, one-hot encoding is straightforward and easy to implement, while embeddings offer a richer representation that captures relationships within the data.