Similarity matrix represents the relationship of n objects and gives us a useful information about these objects. Several models for analyzing this data assume that each object of n objects is embedded as a point or a vector in t dimensional “common space” of n objects in general. However, these models are not appropriate for analyzing a sparse block diagonal similarity matrix as each block diagonal matrix indicates us that each member of the set of objects in a block is represented as a point or a vector in not “common space,” but, “sub-space.” And a model is proposed to analyze this type of a sparse block diagonal similarity matrix. And application to a real data set will be shown.
CITATION STYLE
Imaizumi, T. (2017). Multi-dimensional scaling of sparse block diagonal similarity matrix. In Studies in Classification, Data Analysis, and Knowledge Organization (Vol. 0, pp. 259–272). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-55723-6_20
Mendeley helps you to discover research relevant for your work.