Probabilistic spatial clustering based on the Self Discipline Learning
(SDL) model of autonomous learning
- URL: http://arxiv.org/abs/2201.03449v1
- Date: Fri, 7 Jan 2022 17:18:57 GMT
- Title: Probabilistic spatial clustering based on the Self Discipline Learning
(SDL) model of autonomous learning
- Authors: Zecang Cu, Xiaoqi Sun, Yuan Sun, Fuquan Zhang
- Abstract summary: Unsupervised clustering algorithm can effectively reduce the dimension of high-dimensional unlabeled data.
Traditional clustering algorithm needs to set the upper bound of the number of categories in advance.
Probability spatial clustering algorithm based on the Self Discipline Learning(SDL) model is proposed.
- Score: 1.9322517897534983
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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