English-Chinese Corpus Collection and Artificial Intelligence Translation Algorithm based on Dynamic Clustering and Sparse Representation of Signals

• Published in: 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI) pp. 193 - 197
• Main Authors: An, Jingyu, Huang, Xin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.04.2022
• Subjects: Artificial intelligence; Clustering algorithms; corpus data collection; dynamic clustering; Feature extraction; Heuristic algorithms; Linear algebra; Matrix decomposition; signal processing; Sparse matrices; sparse representation; translation algorithm
• DOI: 10.1109/ICOEI53556.2022.9777108

English-Chinese corpus collection and artificial intelligence translation algorithm based on dynamic clustering and sparse representation of signals is designed in the paper. In linear algebra, the purpose of matrix decomposition is to extract the important features of the matrix, so many sparse linear array design algorithms can reduce the number of array elements through matrix decomposition, hence, for the complex applications, this research study will consider the sparse representation of signals. Semi supervised clustering method considers that the coefficient matrix of data is composed of an auxiliary matrix and the inner product of the constraint matrix with label information, and the constraint matrix with label information should ensure that the data with the same label still have the same coordinates after projection, so as to aggregate the same subspace. The designed algorithm is applied to the scenario of the English-Chinese corpus collection and artificial intelligence translation. Through verification on different database, the performance is validated.