Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, $S^0$, by examining the discriminant power of each input feature. Then, it uses probabilistic projections of features in $S^0$ to yield 1D subspaces and finds the optimal partition for each of them. This is equivalent to partitioning $S^0$ with hyperplanes. A criterion is developed to choose the best $q$ partitions that yield $2q$ partitioned subspaces among them. We assign $S^0$ to the root node of a decision tree and the intersections of $2q$ subspaces to its child nodes of depth one. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops and each leaf node makes a prediction. The idea can be generalized to regression, leading to the subspace learning regressor (SLR). Furthermore, ensembles of SLM/SLR trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM/SLR trees, ensembles and classical classifiers/regressors.
翻译:这项工作中建议采用新的分类模式,称为子空间学习机器(SLM),即所谓的子空间学习机(SLM),这个新的分类模式。SLM首先通过检查每个输入特性的分辨能力,确定一个不相容的子空间,$S=0美元。然后,它使用以美元计算的特性的概率预测,得出1D子空间,并为每个子节点找到最佳分区。这相当于用超平面分隔$S=0美元。制定了一个标准,以选择产生2q美元分解小空间的美元最佳分区。我们为决定树的根节点分配$S=0美元,将2q$子节点相交错至其深度的子节点。在每一个儿童节点反复应用分区进程,以建立可持续土地管理树。当儿童节点的样品足够纯度时,分区进程停止,每个正切点之间的叶节点作出预测。我们为决定树根节点的根节点分配$S=0.0美元,为SLMRS的递归,进行更强的递制。