Classification of small sets of data of large dimension

The problem of classifying of data of very large dimension is considered, while only a limited set of training samples of such data is used. Under these conditions, the possibility of using cause-and-effect relationships in solving classification problems of the specified type is checked. Problem solving is based on the existence of cause-and-effect relationships of unknown causes with the observed partially determined effects of these causes in incoming new data. Training on small set of data is used. The problems are solved in conditions when the size of the data and the number of possible data properties tend to infinity. Asymptotic conditions for unambiguous classification of new data were found. In a particular case, the classification problem was investigated in the presence of random distortions of deterministic effects in the data. The conditions for the possibility of training without a teacher are formulated. The work shows the fundamental possibilities of applying cause-and-effect relationships in the tasks of medical diagnostics, identifying fraudulent schemes in the financial sector, and assessing situational awareness in cybersecurity.