Yani Feng¹, Kejun Tang², Lianxing He³, Pingqiang Zhou¹, Qifeng Liao^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1195-1218, 2023, DOI:10.32604/cmes.2022.021636

Abstract This work proposes a Tensor Train Random Projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically constructed through a Tensor Train (TT) representation with TT-ranks equal to one. Based on the tensor train format, this random projection method can speed up the dimension reduction procedure for high-dimensional datasets and requires fewer storage costs with little loss in accuracy, compared with existing methods. We provide a theoretical analysis of the bias and the variance of TTRP, which shows that this approach is an expected isometric projection with bounded variance, and we show that… More >

E. Kavitha^1,*, R. Tamilarasan², N. Poonguzhali³, M. K. Jayanthi Kannan⁴

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1027-141, 2022, DOI:10.32604/csse.2022.020634

Abstract Gene expression refers to the process in which the gene information is used in the functional gene product synthesis. They basically encode the proteins which in turn dictate the functionality of the cell. The first step in gene expression study involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise and imprecision. For a biological system to function, the essential cellular molecules must interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will… More >