The rapid developments of technologies that generate arrays of gene dataenable a global view of the transcription levels of hundreds of thousands of genes simultaneously.The outlier detection problem for gene data has...The rapid developments of technologies that generate arrays of gene dataenable a global view of the transcription levels of hundreds of thousands of genes simultaneously.The outlier detection problem for gene data has its importance but together with the difficulty ofhigh dimensionality. The sparsity of data in high-dimensional space makes each point a relativelygood outlier in the view of traditional distance-based definitions. Thus, finding outliers in highdimensional data is more complex. In this paper, some basic outlier analysis algorithms arediscussed and a new genetic algorithm is presented. This algorithm is to find best dimensionprojections based on a revised cell-based algorithm and to give explanations to solutions. It cansolve the outlier detection problem for gene expression data and for other high dimensional data aswell.展开更多
文摘The rapid developments of technologies that generate arrays of gene dataenable a global view of the transcription levels of hundreds of thousands of genes simultaneously.The outlier detection problem for gene data has its importance but together with the difficulty ofhigh dimensionality. The sparsity of data in high-dimensional space makes each point a relativelygood outlier in the view of traditional distance-based definitions. Thus, finding outliers in highdimensional data is more complex. In this paper, some basic outlier analysis algorithms arediscussed and a new genetic algorithm is presented. This algorithm is to find best dimensionprojections based on a revised cell-based algorithm and to give explanations to solutions. It cansolve the outlier detection problem for gene expression data and for other high dimensional data aswell.
