论文标题
高维数据的多项式集群加权模型
Multinomial Cluster-Weighted Models for High-Dimensional Data
论文作者
论文摘要
高维数据的建模对于对不同类别进行分类非常重要。我们开发了一种称为多项式集群加权模型（MCWM）的新混合模型。我们得出了MCWM一般类别的可识别性。我们通过迭代重新加权的最小二乘（EM-irls）和随机梯度下降（EM-SGD）来估计提出的模型（EM）算法（EM-SGD）。使用不同的信息标准进行模型选择。各种调整后的兰特指数被认为是准确性的不同量度。使用模拟和真实数据集研究了提出模型的聚类性能。 MCWM通过绩效指标显示出极好的聚类结果，例如ROC曲线下的精度和面积。
Modeling of high-dimensional data is very important to categorize different classes. We develop a new mixture model called Multinomial cluster-weighted model (MCWM). We derive the identifiability of a general class of MCWM. We estimate the proposed model through Expectation-Maximization (EM) algorithm via an iteratively reweighted least squares (EM-IRLS) and Stochastic Gradient Descent (EM-SGD). Model selection is carried out using different information criteria. Various Adjusted Rand Indices are considered as a different measure of accuracy. The clustering performance of the proposed model is investigated using simulated and real datasets. MCWM shows excellent clustering results via performance measures such as Accuracy and Area under the ROC curve.
