# Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning

arXiv: https://arxiv.org/abs/2201.03449 · HTML: https://mlsanity.com/paper/2201.03449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Representation Learning by Learning to Count](https://mlsanity.com/paper/1708.06734/) (2017)
- [Dimensionality Reduction using Similarity-induced Embeddings](https://mlsanity.com/paper/1706.05692/) (2017)
- [Distance formulas capable of unifying Euclidian space and probability space](https://mlsanity.com/paper/1801.01972/) (2018)
- [The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving](https://mlsanity.com/paper/1812.03007/) (2018)

## Abstract (arXiv)

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.