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特征学习的动力来自于机器学习任务，如分类中，通常需要数学上和计算上方便处理的输入。然而，真实世界的数据，如图像、视频和感官数据，并没有那么简单就可以用通过算法定义特定特征。另一种方法是通过检查发现这些特征或表示，而不依赖于显式算法。

特征学习的动力来自于机器学习任务，如分类中，通常需要数学上和计算上方便处理的输入。然而，真实世界的数据，如图像、视频和感官数据，并没有那么简单就可以用通过算法定义特定特征。另一种方法是通过检查发现这些特征或表示，而不依赖于显式算法。

==== Sparse dictionary learning ====

==== 稀疏字典学习 Sparse dictionary learning ====

{{Main|Sparse dictionary learning}}

{{Main|Sparse dictionary learning}}

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.

稀疏词典学习是一种特征学习方法，在这种方法中，一个训练例子被表示为基函数的线性组合，并假设为稀疏矩阵。该方法具有强 np- 困难性，近似求解困难。一种流行的启发式稀疏字典学习方法是 K-SVD 算法。稀疏词典学习已经应用于几种情况下。在分类中，问题在于确定先前未见的训练示例所属的类。对于已经构建了每个类的字典，一个新的训练示例将与相应的字典最好地稀疏表示的类相关联。稀疏字典学习也被应用到图像去噪中。其关键思想是，一个干净的图像补丁可以由图像字典稀疏表示，但噪声不能。

稀疏词典学习是一种特征学习方法，在这种方法中，一个训练样本被表示为基函数的线性组合，并假设为稀疏矩阵。该方法具有强 NP- 困难性并且近似求解困难。一种流行的'''启发式 Heuristic'''稀疏字典学习方法是 K-SVD 算法。稀疏词典学习已经应用于以下几种情况下：在分类中，问题在于如何确定先前未见的训练样本所属的类；对于已经构建了每个类的字典，一个新的训练示例将与相应的字典最好地稀疏表示的类相关联。稀疏字典学习也被广泛应用到图像去噪的问题中。其关键思想是，一个干净的图像'''补丁 patch'''可以由图像字典稀疏地表示，但噪声不能。

 

 

==== Anomaly detection ====

==== Anomaly detection ====