更改

Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

一些学习算法旨在发现更好的训练数据输入的对应表示，其典型的例子包括'''主成分分析 Principal components analysis'''和'''聚类分析 Cluster analysis'''。'''特征学习 Feature learning'''算法，也称为'''表征学习 Representation learning'''算法，通常试图保留输入中的信息，但也可以使用有效的方式对输入进行转换从而达到提升学习效率和效果的目的，通常作为执行分类或预测行为之前的预处理步骤。这种技术可以重构来自未知数据分布生成的输入，但不一定忠实于在这种分布下不可信的配置。这取代了手工特性工程，并且允许机器学习特性并使用它们来执行特定的任务。

一些学习算法旨在发现更好的训练数据输入的对应表示，其典型的例子包括'''主成分分析 Principal Components Analysis'''和'''聚类分析 Cluster Analysis'''。'''特征学习 Feature Learning'''算法，也称为'''表征学习 Representation Learning'''算法，通常试图保留输入中的信息，但也可以使用有效的方式对输入进行转换从而达到提升学习效率和效果的目的，通常作为执行分类或预测行为之前的预处理步骤。这种技术可以重构来自未知数据分布生成的输入，但不一定忠实于在这种分布下不可信的配置。这取代了手工特性工程，并且允许机器学习特性并使用它们来执行特定的任务。

{} / ref

{} / ref

特征学习可以是有监督的，也可以是无监督的。在有监督的特征学习中，可以利用标记输入数据学习特征。例如'''人工神经网络 Artificial neural networks，ANN'''、'''多层感知机 Multilayer perceptrons，MLP'''和受控字典式学习模型 Supervised dictionary learning model，SDLM。在无监督的特征学习中，特征是通过未标记的输入数据进行学习的。例如，'''字典学习 Dictionary learning'''、'''独立元素分析 Independent component analysis'''、'''自动编码器 Autoencoders'''、'''矩阵分解 Matrix factorization'''和各种形式的聚类。

特征学习可以是有监督的，也可以是无监督的。在有监督的特征学习中，可以利用标记输入数据学习特征。例如'''人工神经网络 Artificial Neural Networks，ANN'''、'''多层感知机 Multilayer Perceptrons，MLP'''和受控字典式学习模型 Supervised Dictionary Learning Model，SDLM。在无监督的特征学习中，特征是通过未标记的输入数据进行学习的。例如，'''字典学习 Dictionary learning'''、'''独立元素分析 Independent Component Analysis'''、'''自动编码器 Autoencoders'''、'''矩阵分解 Matrix Factorization'''和各种形式的聚类。

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

'''流形学习 Manifold learning'''算法试图在学习表示为低维的约束条件下进行流形学习。'''稀疏编码算法 Sparse coding algorithms'''试图在学习表示为稀疏的约束条件下进行编码，这意味着数学模型有许多'''零点 Zeros'''。'''多线性子空间学习算法 Multilinear subspace learning algorithms'''旨在直接从多维数据的张量表示中学习低维的表示，而不是将它们重塑为高维向量。'''深度学习算法 Deep learning algorithms'''发现了多层次的表示，或者是一个特征层次结构，具有更高层次、更抽象的特征，这些特征定义为（或可以生成）低层次的特征。有人认为，一个智能机器的表现是可以学习到一种表示的方法，并能够解释数据观测值变化背后的机理或潜在影响。

'''流形学习 Manifold Learning'''算法试图在学习表示为低维的约束条件下进行流形学习。'''稀疏编码算法 Sparse Coding Algorithms'''试图在学习表示为稀疏的约束条件下进行编码，这意味着数学模型有许多'''零点 Zeros'''。'''多线性子空间学习算法 Multilinear Subspace Learning Algorithms'''旨在直接从多维数据的张量表示中学习低维的表示，而不是将它们重塑为高维向量。'''深度学习算法 Deep Learning Algorithms'''发现了多层次的表示，或者是一个特征层次结构，具有更高层次、更抽象的特征，这些特征定义为（或可以生成）低层次的特征。有人认为，一个智能机器的表现是可以学习到一种表示的方法，并能够解释数据观测值变化背后的机理或潜在影响。