# The Exact Solution to Rank-1 L1-norm TUCKER2 Decomposition

arXiv: https://arxiv.org/abs/1710.11306 · HTML: https://mlsanity.com/paper/1710.11306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Robust Visual Tracking by Motion Analyzing](https://mlsanity.com/paper/2309.03247/) (2023)

## Abstract (arXiv)

We study rank-1 L1-norm-based TUCKER2 (L1-TUCKER2) decomposition of 3-way tensors, treated as a collection of N D × M matrices that are to be jointly decomposed. Our contributions are as follows. i) We prove that the problem is equivalent to combinatorial optimization over N antipodal-binary variables. ii) We derive the first two algorithms in the literature for its exact solution. The first algorithm has cost exponential in N; the second one has cost polynomial in N (under a mild assumption). Our algorithms are accompanied by formal complexity analysis. iii) We conduct numerical studies to compare the performance of exact L1-TUCKER2 (proposed) with standard HOSVD, HOOI, GLRAM, PCA, L1-PCA, and TPCA-L1. Our studies show that L1-TUCKER2 outperforms (in tensor approximation) all the above counterparts when the processed data are outlier corrupted.