Abstract
The recent explosion of publicly available biology gene sequences and chemical compounds offers an unprece-dented opportunity for data mining. To make data analysis feasible for such vast volume and high-dimensional scientific data, we apply high performance dimension reduction algorithms. It facilitates the investigation of unknown structures in a three dimensional visualization. Among the known dimension reduction algorithms, we utilize the multidimensional scaling (MDS) algorithm to configure the given high-dimensional or abstract data into a target di-mension. However, the MDS algorithm requires large physical memory as well as computational resources. In order to reduce computational complexity and memory requirement effectively, the interpolation method of the MDS was proposed in 2010. With minor trade-off of approximation, the MDS interpolation method enables us to process mil-lions of data points with modest amounts of computation and memory requirement. In this paper, we would like to improve the mapping quality of the MDS interpolation approach by adapting the original dissimilarity based on the ratio between the original dissimilarity and the corresponding mapping distances. Our experimental results illustrate that the quality of interpolated mapping results are improved by adding the adaptation step without runtime loss com-pared to the original interpolation method. With the proposed adaptive interpolation method, we construct a better configuration of millions of out-of-sample data into a target dimension than the previous interpolation method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.