Computational efficiency in symbolic sequence analysis using random sequence embedding

摘要

Methods and systems for analyzing symbol sequences are provided.Receive the metadata of the symbol sequence from the owner's computing device.Based on the received metadata, a set of R random sequences is generated and sent to the owner's computing device for the computation of feature matrices based on the set of R random sequences and symbol sequences.The feature matrix is received from the owner's computing device of the symbol sequence.If it is determined that the inner product of the feature matrix is below the threshold precision, the iteration process returns to the generation of R random sequences.If the inner product of the feature matrix is determined to be above the threshold, the feature matrix is categorized based on machine learning.A categorized global feature matrix is sent to be displayed on the user interface of the computing device of the owner of the symbol sequence.