Vector embeddings: Difference between revisions

Vector embeddings are a crucial and fascinating aspect of machine learning, playing a central role in numerous natural language processing (NLP), recommendation, and search algorithms. These embeddings enable systems such as recommendation engines, voice assistants, and language translators to function effectively. Machine learning algorithms, like other software algorithms, require numerical data to operate. Vector embeddings are lists of numbers that represent more abstract data types, such as text documents or other non-numeric objects, facilitating various operations. The use of vector embeddings allows for the translation of human-perceived semantic similarity into proximity within a vector space.

 

When real-world objects and concepts like images, audio recordings, news articles, user profiles, weather patterns, and political views are represented as vector embeddings, their semantic similarity can be quantified by how close they are to each other as points in vector spaces. This representation is suitable for common machine learning tasks, such as clustering, recommendation, and classification.

 

In clustering tasks, for example, algorithms assign similar points to the same cluster while keeping points from different clusters as dissimilar as possible. In recommendation tasks, recommender systems look for objects most similar to the target object, as measured by their similarity in vector embeddings. In classification tasks, the label of an unseen object is determined by the majority vote over the labels of the most similar objects.

One method for creating vector embeddings involves engineering the vector values using domain knowledge, a process known as feature engineering. For instance, in medical imaging, domain expertise is employed to quantify features such as shape, color, and regions within an image to capture semantics. However, feature engineering requires domain knowledge and is often too costly to scale.

Rather than engineering vector embeddings, models are frequently trained to translate objects into vectors. Deep neural networks are commonly used for training such models. The resulting embeddings are typically high-dimensional (up to two thousand dimensions) and dense (all values are non-zero). Text data can be transformed into vector embeddings using models such as Word2Vec, GLoVE, and BERT. Images can be embedded using convolutional neural networks (CNNs) like VGG and Inception, while audio recordings can be converted into vectors using image embedding transformations over their visual representations, such as spectrograms.