Embedding is a critical concept in machine learning, particularly in handling categorical data or natural language processing. It is a process of transforming a high-dimensional, sparse object into a low-dimensional, dense vector in a way that preserves its semantic relationships or inherent characteristics.
The resulting vector is known as an "embedding vector" or simply "embedding," which represents the input data in a new feature space.
How Embedding works
Embedding works by mapping each unique object to a distinct vector in a continuous vector space. The distance between vectors could indicate the similarity between objects they represent. For example, in word embedding, each word in a vocabulary is mapped to a unique vector such that words with similar meanings are located close to each other in the embedding space.
The mapping function or the transformation is learned in a way that it maximizes the ability to perform a specific task. For instance, in a text classification task, the embedding vectors are learned by backpropagation in conjunction with the rest of the model parameters such that it improves the classification accuracy.
One of the most common types of embedding is word embedding, where words are mapped to vectors of real numbers. Techniques like Word2Vec, GloVe, and FastText are often used to compute word embeddings. These methods capture semantic relationships between words, such as "man" is to "woman" as "king" is to "queen"; in the embedding space, 'man' vector subtracted from 'king' vector is approximately equal to 'woman' vector subtracted from 'queen' vector.
In a broader sense, embedding can be used to convert any high-dimensional data into lower-dimensional space, which might include transforming users and items for recommendation systems, nodes in a graph for graph-based algorithms, or even whole sentences or documents for complex NLP tasks. This helps in overcoming the curse of dimensionality and aids in tasks like visualization, clustering, or feeding categorical data to machine learning algorithms.
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