What is the difference between embeddings and one-hot encoding?

Embeddings and one-hot encoding are techniques to represent categorical or textual data numerically, but they differ fundamentally in structure and application. One-hot encoding converts discrete categories into sparse binary vectors where only one element is “1” (indicating the category), and the rest are "0". For example, a category like “animal” with values “cat,” “dog,” and “bird” would be represented as [1,0,0], [0,1,0], and [0,0,1], respectively. This method is simple and deterministic but scales poorly with high-dimensional data. Embeddings, on the other hand, map data into dense, lower-dimensional vectors where each dimension captures latent features (e.g., semantic meaning). These vectors are learned through training, allowing similar items to have numerically close representations. For instance, in natural language processing (NLP), the word “king” might be embedded as [0.8, -0.3, 0.2], while “queen” could be [0.7, -0.2, 0.1], reflecting their semantic similarity.

The key distinction lies in how relationships between categories are handled. One-hot encoding treats all categories as independent and equidistant—there’s no inherent notion of similarity. For example, “cat” and “dog” are as distinct as “cat” and “car” in one-hot space. Embeddings, however, capture meaningful relationships. In a trained embedding layer, words or categories with related meanings (e.g., “cat” and “dog”) occupy closer positions in the vector space. This makes embeddings especially useful for tasks like recommendation systems or NLP, where understanding context or similarity is critical. For example, in a movie recommendation model, embeddings could group “action” and “adventure” genres closer together than “action” and “documentary,” improving recommendation accuracy.

From a practical standpoint, one-hot encoding is ideal for small, fixed sets of categories (e.g., encoding “yes/no” flags or low-cardinality features like country codes). However, it becomes inefficient for large vocabularies (e.g., all English words) due to memory and computational costs. Embeddings address this by compressing information into a fixed-size vector (e.g., 128 dimensions), regardless of vocabulary size. In frameworks like TensorFlow or PyTorch, embeddings are implemented as trainable layers in neural networks, allowing models to learn meaningful representations during training. For instance, when processing text, an embedding layer can transform each word into a dense vector that the model uses to detect patterns. While one-hot encoding is static and requires no training, embeddings are dynamic and adapt to the data, making them more powerful but also computationally heavier to train.