Reconstruction of fragmented trajectories of collective motion using Hadamard deep autoencoders

•Interactions in collective motion (CM) ensure their trajectory matrix is low-rank.•Low-rankness of CM is the pattern learned by our Hadamard deep autoencoder (HDA).•HDA incorporates an indicator matrix into the loss function using Hadamard product.•HDA is trained only using the observed trajectory segments.•Performance of HDA is validated against a low-rank matrix completion framework.