Embedding in
machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
refers to a
representation learning technique that maps complex, high-dimensional data into a lower-dimensional
vector space
In mathematics and physics, a vector space (also called a linear space) is a set (mathematics), set whose elements, often called vector (mathematics and physics), ''vectors'', can be added together and multiplied ("scaled") by numbers called sc ...
of numerical vectors.
It also denotes the resulting representation, where meaningful patterns or relationships are preserved. As a technique, it learns these vectors from data like words, images, or user interactions, differing from manually designed methods such as
one-hot encoding.
This process reduces complexity and captures key features without needing prior knowledge of the problem area (domain).
For example, in
natural language processing
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related ...
(NLP), it might represent "cat" as
.2, −0.4, 0.7 "dog" as
.3, −0.5, 0.6 and "car" as
.8, 0.1, −0.2 placing "cat" and "dog" close together in the space—reflecting their similarity—while "car" is farther away. The resulting embeddings vary by type, including
word embedding
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that ...
s for text (e.g.,
Word2Vec
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these rep ...
),
image embeddings for visual data, and
knowledge graph embeddings for
knowledge graph
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a Graph (discrete mathematics), graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interl ...
s, each tailored to tasks like NLP,
computer vision
Computer vision tasks include methods for image sensor, acquiring, Image processing, processing, Image analysis, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical ...
, or
recommendation systems.
This dual role enhances model efficiency and accuracy by automating feature extraction and revealing latent similarities across diverse applications.
See also
*
Feature extraction
Feature may refer to:
Computing
* Feature recognition, could be a hole, pocket, or notch
* Feature (computer vision), could be an edge, corner or blob
* Feature (machine learning), in statistics: individual measurable properties of the phenome ...
*
Dimensionality reduction
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally ...
*
Word embedding
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that ...
*
Neural network
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways. While individual neurons are simple, many of them together in a network can perfor ...
*
Reinforcement learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learnin ...
References
{{Compu-AI-stub
Machine learning
Artificial neural networks
Natural language processing