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In machine learning, feature learning or representation learning is a set of techniques that learn a feature: a transformation of raw data input to a representation that can be effectively exploited in machine learning tasks. This obviates the need for manual feature engineering, which is otherwise necessary, and allows a machine to both learn at a specific task (''using'' the features) ''and'' learn the features themselves: to learn how to learn. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor measurement is usually complex, redundant, and highly variable. Thus, it is necessary to discover useful features or representations from raw data. Traditional hand-crafted features often require expensive human labor and often rely on expert knowledge. Also, they normally do not generalize well. This motivates the design of efficient feature learning techniques, to automate and generalize this. Feature learning can be divided into two categories: supervised and unsupervised feature learning, analogous to these categories in machine learning generally. *In supervised feature learning, features are learned with labeled input data. Examples include neural networks, multilayer perceptron, and (supervised) dictionary learning. *In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization, and various forms of clustering.〔 == Supervised feature learning == Supervised feature learning is to learn features from labeled data. Several approaches are introduced in the following. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「feature learning」の詳細全文を読む スポンサード リンク
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