基于低秩特征的双变换矩阵学习图像分类
Low-rank features based double transformation matrices learning for image classification
摘要：
线性回归是一种监督方法，已广泛用于分类任务。为了将线性回归应用于分类任务，提出了一种松弛回归目标的技术。然而，基于该技术的方法忽略了由于数据中包含的复杂信息而对单个变换矩阵的压力。在这种情况下，单一的变换矩阵过于严格，无法提供灵活的投影，因此有必要对变换矩阵采用松弛。提出了一种基于潜在低秩特征提取的双变换矩阵学习方法。其核心思想是使用双变换矩阵进行松弛，将学习到的主特征和显著特征从两个方向联合投影到标签空间中，从而分担单个变换矩阵的压力。首先，低秩特征学习的潜在低秩表示（LatLRR）方法，从两个方向处理的原始数据。在这个过程中，稀疏噪声也被分离出来，这在一定程度上消除了它对投影学习的干扰。然后，引入两个变换矩阵分别处理这两个特征，提取对分类有用的信息。最后，这两个转换矩阵可以很容易地通过交替优化方法获得。通过这样的处理，即使当样本中包含大量冗余信息时，我们的方法也可以获得易于分类的投影结果。在多个数据集上的实验证明了该方法的有效性，特别是对于复杂的场景。
英文摘要：
Linear regression is a supervised method that has been widely used in classification tasks. In order to apply linear regression to classification tasks, a technique for relaxing regression targets was proposed. However, methods based on this technique ignore the pressure on a single transformation matrix due to the complex information contained in the data. A single transformation matrix in this case is too strict to provide a flexible projection, thus it is necessary to adopt relaxation on transformation matrix. This paper proposes a double transformation matrices learning method based on latent low-rank feature extraction. The core idea is to use double transformation matrices for relaxation, and jointly projecting the learned principal and salient features from two directions into the label space, which can share the pressure of a single transformation matrix. Firstly, the low-rank features are learned by the latent low rank representation (LatLRR) method which processes the original data from two directions. In this process, sparse noise is also separated, which alleviates its interference on projection learning to some extent. Then, two transformation matrices are introduced to process the two features separately, and the information useful for the classification is extracted. Finally, the two transformation matrices can be easily obtained by alternate optimization methods. Through such processing, even when a large amount of redundant information is contained in samples, our method can also obtain projection results that are easy to classify. Experiments on multiple data sets demonstrate the effectiveness of our approach for classification, especially for complex scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
