论文标题
基于自主学习的自我纪律学习（SDL）模型的概率空间聚类
Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning
论文作者
论文摘要
无监督的聚类算法可以有效地降低高维无标记数据的维度，从而降低数据处理的时间和空间复杂性。但是，传统的聚类算法需要提前设置类别数量的上限，并且深度学习聚类算法将属于局部最佳的问题。为了解决这些问题，提出了基于自我纪律学习（SDL）模型的概率空间聚类算法。该算法基于向量之间概率空间距离的高斯概率分布，并使用概率量表和概率空间距离的最大概率值作为距离测量判断，然后根据数据集本身的分布特征来确定每个样本的类别。该算法在实验室中测试了智能和安全汽车（LISA）交通信号灯数据集，准确率为99.03％，召回率为91％，并实现了效果。
Unsupervised clustering algorithm can effectively reduce the dimension of high-dimensional unlabeled data, thus reducing the time and space complexity of data processing. However, the traditional clustering algorithm needs to set the upper bound of the number of categories in advance, and the deep learning clustering algorithm will fall into the problem of local optimum. In order to solve these problems, a probabilistic spatial clustering algorithm based on the Self Discipline Learning(SDL) model is proposed. The algorithm is based on the Gaussian probability distribution of the probability space distance between vectors, and uses the probability scale and maximum probability value of the probability space distance as the distance measurement judgment, and then determines the category of each sample according to the distribution characteristics of the data set itself. The algorithm is tested in Laboratory for Intelligent and Safe Automobiles(LISA) traffic light data set, the accuracy rate is 99.03%, the recall rate is 91%, and the effect is achieved.
