Earticle

This paper mainly studies a kind of different from the traditional signal processing theory, compressed sensing. The theory in the process of data transmission greatly save the space and the cost of storage and transmission is a new breakthrough in the data mining technology. Application of compressed sensing principle, the discrete tracking algorithm combined with matrix learning algorithm, to deal with the noise of original image. Through a large number of experiments show that the fusion algorithm is better than other similar algorithms in terms of denoising, is a better image denoising technique.

In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote the connection in the BN structure. Further, new genetic operators are developed to implement the MGA. The genetic operators are closed in the set of the directed acyclic graph (DAG). Finally, the proposed scheme is applied to real world and benchmark applications, and its effectiveness is demonstrated through computer simulation.