The parallel decoding method of a parallel concatenation of multiple codes is well known. In this paper, we present a new serial decoding method. The iterative gain in this method is always one. Therefore, this method...The parallel decoding method of a parallel concatenation of multiple codes is well known. In this paper, we present a new serial decoding method. The iterative gain in this method is always one. Therefore, this method does not need optimization of the iterative gain by using simulated annealing like the parallel decoding method. Though it is simpler than the parallel decoding method in calculation, it gives the same performance. We also use Pearl's propagation algorithm to show the appropriateness of the serial decoding method.展开更多
Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feat...Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.展开更多
文摘The parallel decoding method of a parallel concatenation of multiple codes is well known. In this paper, we present a new serial decoding method. The iterative gain in this method is always one. Therefore, this method does not need optimization of the iterative gain by using simulated annealing like the parallel decoding method. Though it is simpler than the parallel decoding method in calculation, it gives the same performance. We also use Pearl's propagation algorithm to show the appropriateness of the serial decoding method.
基金Supported by the National Natural Science Foundation of China (No. 50877004)
文摘Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.
