Phylogenetic Minimum Spanning Tree Reconstruction Using Autoencoders

The history of a shared and re-posted multimedia content can be reconstructed by analyzing the mutual relations between all of its near-duplicate copies and solving a minimum spanning tree (MST) problem, as shown by multimedia phylogeny research field. Unfortunately, MST estimation strategies are severely impaired by the noise affecting dissimilarity measures between pairs of near-duplicate contents. For this reason, researchers have recently been investigating robust dissimilarity metrics. This paper proposes a matrix denoising solution that both mitigates dissimilarity noise and reconstruct the desired phylogenetic tree at the same time. The proposed strategy is a first attempt to estimate a MST via a denoising autoencoder that returns an approximation of the adjacency matrix corresponding to the underlying tree. Experimental results prove that the proposed solution outperforms the previous approaches and easily adapts to different analysis scenarios.