 Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasetsPeter Wittek Computational Statistics & Data Analysis, 2013, vol. 66, issue C, 193-201 Abstract: Two-way seriation is a popular technique to analyze groups of similar instances and their features, as well as the connections between the groups themselves. The two-way seriated data may be visualized as a two-dimensional heat map or as a three-dimensional landscape where colour codes or height correspond to the values in the matrix. To achieve a meaningful visualization of high-dimensional data, a compactly supported convolution kernel is introduced, which is similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional space with values that interpolate nearby elements and provides insight into the clustering structure. Ordinary two-way seriation is also extended to deal with updates of both the row and column spaces. Combined with the convolution kernel, a three-dimensional visualization of dynamics is demonstrated on two datasets, a news collection and a set of microarray measurements. Keywords: Two-way seriation; Gaussian filtering; Landscape visualization; High-dimensional data; Hamiltonian path (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:66:y:2013:i:c:p:193-201 DOI: 10.1016/j.csda.2013.03.026 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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