News Event Understanding by Mining Latent Factors From Multimodal Tensors

Abstract

We present a novel and efficient constrained tensor factorization algorithm that first represents a video archive, of multimedia news stories concerning a news event, as a sparse tensor of order 4. The dimensions correspond to extracted visual memes, verbal tags, time periods and cultures. The iterative algorithm then approximately but accurately ex- tracts… (More)
DOI: 10.1145/2983563.2983564

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