Algorithms for an Efficient Tensor Biclustering
This addresses data analysis challenges in fields like bioinformatics or social sciences where temporal patterns in multi-dimensional data need to be identified, though it appears incremental as it builds on spectral decomposition methods.
The paper tackles the tensor biclustering problem for data with individuals, features, and time dimensions by computing subsets where signal trajectories lie in a low-dimensional subspace, and it evaluates the algorithms on synthetic and real datasets.
Consider a data set collected by (individuals-features) pairs in different times. It can be represented as a tensor of three dimensions (Individuals, features and times). The tensor biclustering problem computes a subset of individuals and a subset of features whose signal trajectories over time lie in a low-dimensional subspace, modeling similarity among the signal trajectories while allowing different scalings across different individuals or different features. This approach are based on spectral decomposition in order to build the desired biclusters. We evaluate the quality of the results from each algorithms with both synthetic and real data set.