Unsupervised Learning for Tracking and Classification of Sequences

Sammanfattning

Everyday use of artificial intelligence in the form of large language models(LLMs) has become a noticeable part of life for many. This emphasises the importance ofeffective machine learning models working in an unsupervised learning setup. This paperaims to examine such a model, the recently proposed DANSE model. Specifically, the modelwas adapted for the classification of noisy sequences and compared to an RNN (recurrentneural network) with the same purpose. The sequences analysed were two different three-dimensional trajectories, the Lorenz and Chen attractors. To adapt the proposed model, theDANSE model was trained once for each of the attractors. Furthermore, a maximum-likelihood function was applied, and a prediction was made. The RNN model wasimplemented for classification as a baseline. The results were compared by calculatingaccuracy, recall, precision, and F1 score from a confusion matrix. The results showed that theRNN classifier was more effective for the attractors with more noise. However, with lessnoise, the DANSE model showed better and more consistent results.