Two-way incremental seriation in the temporal domain with three-dimensional visualization: making sense of evolving high-dimensional datasets. (English) Zbl 1471.62217
Summary: 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.
MSC:
| 62-08 | Computational methods for problems pertaining to statistics |
| 62H35 | Image analysis in multivariate analysis |
Keywords:
two-way seriation; Gaussian filtering; landscape visualization; high-dimensional data; Hamiltonian pathReferences:
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