Research And Application On Orbits Generated Algorithm Of Learning Subspace In Lie-Group Machine Learning (LML)

Machine learning is an external topic in the researches of artificial intelligence and computer science, thus, the tendency of machine learning is that more and more mathematicians will participate in and construct learning methods with solid theoretical foundation. Based on LML theory frame and its algebraic model, geometric model and learning axiom systems, going for further study, this paper presented orbits generated algorithm of learning subspace in LML and applied it to corresponding examples, such as classify of human, chemical composition of wine and eight data sets including Soybean-Large, etc.Therefore, the main characteristics of this paper are:(1) Given orbits generated correlation theory of learning subspace which is the base of researching algorithm;(2) Advanced orbits generated Breadth first, Depth first and Heuristic algorithms, enrich and develop the basic content of LML further;(3) Given corresponding examples to validate the algorithms.However, all the work is tentive and much needs advanced research. Based on the work developed, the following possibilities of future work have been identified: orbits generated algorithm of learning subspace under unitary group and odd & even orthogonal group, orbits generated net, and so on.