# Algorithms for an Efficient Tensor Biclustering

## TL;DR

This paper introduces spectral decomposition algorithms for tensor biclustering, identifying subsets of individuals and features with similar signal trajectories over time, validated on synthetic and real datasets.

## Contribution

It proposes novel spectral decomposition-based algorithms for tensor biclustering that effectively identify low-dimensional biclusters in multi-dimensional data.

## Key findings

- Algorithms successfully identify meaningful biclusters
- Effective on both synthetic and real datasets
- Outperforms existing methods in accuracy

## Abstract

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.