论文标题
使用加权正交回归法的信号分类
Signal classification using weighted orthogonal regression method
论文作者
论文摘要
在本文中，提出了基于数据的内在属性的新分类器。分类是基于数据挖掘的应用程序中的重要任务。当训练集的大小不足以与问题的维度相提并论时，分类问题将具有挑战性。本文提出了一种新的分类方法，该方法通过相应的特征组件利用每个类的内在结构。每个组件通过特定的重量有助于每个班级学习的跨度。重量由相关特征值确定。在培训数据有限的情况下，这种方法在面临分类问题的情况下，可靠地学习可靠。所提出的方法涉及通过每个类的数据获得的SVD获得的特征向量，以选择每个子空间的碱基。此外，它考虑了决策标准的有效加权，以区分两个类别。除了在人工数据上高性能外，该方法还增加了国际竞争的最佳结果。
In this paper, a new classifier based on the intrinsic properties of the data is proposed. Classification is an essential task in data mining-based applications. The classification problem will be challenging when the size of the training set is not sufficient to compare to the dimension of the problem. This paper proposes a new classification method that exploits the intrinsic structure of each class through the corresponding Eigen components. Each component contributes to the learned span of each class by specific weight. The weight is determined by the associated eigenvalue. This approach results in reliable learning robust in the case of facing a classification problem with limited training data. The proposed method involves the obtained Eigenvectors by SVD of data from each class to select the bases for each subspace. Moreover, it considers an efficient weighting for the decision-making criterion to discriminate two classes. In addition to high performance on artificial data, this method has increased the best result of international competition.
