Feature extraction is an essential component in many classification tasks. Popular feature extraction approaches especially deep learning-based methods, need large training samples to achieve satisfactory performance. Although dictionary learning-based methods are successfully used for feature extraction on both small and large datasets, however, when dealing with high-dimensional datasets, a large number of dimensions also mask the discriminative information embedded in the data. To address these issues, a novel feature learning framework for high-dimensional data classification is proposed in this paper. Specially, to discard the irrelevant parts that derail the dictionary learning process, the dictionary is adaptively learnt in the low-dimensional space parameterized by a transformation matrix. To ensure that the learned features are discriminative for the classifier, the classification results in turn are used to guide the dictionary and transformation matrix learning process. Compared with other methods, the proposed method simultaneously exploits the dimension reduction, dictionary learning and classifier learning in one optimization framework, which enables the method to extract low-dimensional and discriminative features. Experimental results on several benchmark datasets demonstrate the superior performance of the proposed method for high-dimensional data classification task, particularly when the number of training samples is small.

特征提取是许多分类任务中必不可少的组成部分。流行的特征提取方法（尤其是基于深度学习的方法）需要大量的训练样本才能获得令人满意的性能。尽管基于字典学习的方法已成功用于小型和大型数据集的特征提取，但是，当处理高维数据集时，大量维也掩盖了嵌入数据中的区分性信息。为了解决这些问题，本文提出了一种新颖的用于高维数据分类的特征学习框架。特别地，为了丢弃使字典学习过程脱轨的不相关部分，在由变换矩阵参数化的低维空间中自适应地学习字典。为了确保所学习的特征对于分类器具有区别性，分类结果又用于指导字典和变换矩阵的学习过程。与其他方法相比，该方法在一个优化框架中同时利用了降维，字典学习和分类器学习，这使得该方法能够提取低维和判别特征。在几个基准数据集上的实验结果证明了该方法在高维数据分类任务中的优越性能，尤其是在训练样本数量较少时。该方法在一个优化框架中同时利用了降维，字典学习和分类器学习，使得该方法能够提取低维和判别特征。在几个基准数据集上的实验结果证明了该方法在高维数据分类任务中的优越性能，尤其是在训练样本数量较少时。该方法在一个优化框架中同时利用了降维，字典学习和分类器学习，使得该方法能够提取低维和判别特征。在几个基准数据集上的实验结果证明了该方法在高维数据分类任务中的优越性能，尤其是在训练样本数量较少时。