Distributed Representation is a type of representation used in machine learning that encodes knowledge in a neural network as a set of real-valued vectors. It is an important component of deep learning and is used to represent words, phrases, and other types of text in a way that allows for automatic performance of tasks such as sentiment analysis, object classification, and language translation. This type of representation is also used in the fields of natural language processing and image recognition.
The idea of distributed representation was first proposed by Hinton and Rumelhart (1986). They suggested that a network of neurons could learn patterns of representation from sensory input, enabling it to perform tasks such as classification and pattern recognition. This type of representation is particularly powerful because it allows for the transfer of knowledge from one task to another, which is otherwise difficult to achieve using a traditional single layer approach.
Distributed representation is based on the idea of representing knowledge as a set of real-valued vectors. Each vector is composed of a set of elements, each of which is associated with a particular concept or idea, such as a single word or phrase. These vectors are then used to encode the relationships between different concepts. For example, a vector may represent the relationship between a word and its definition, or between two related words.
In order to learn the representations, the neural network must be able to determine which elements of the vector are most important for a particular task. This is typically done through a process of training, where the network is presented with a set of input data and the desired output. As the network processes the data, it adjusts the weights of the elements in the vector, resulting in a representation that is most applicable to the task at hand.
Distributed representation is a powerful tool for understanding the relationships between different concepts, and for solving complex tasks. It has been used in numerous applications, such as natural language processing, image recognition, and sentiment analysis. In addition, distributed representation allows for the transfer of knowledge from one task to another, making it an important component of deep learning.
References
Hinton, G. E., & Rumelhart, D. E. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Khan, A. U., & Zhang, M. (2018). Distributed Representation in Natural Language Processing: A Comprehensive Survey. IEEE Access, 6, 12133-12154.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119).