  We present a novel nonnegative tensor decomposition method, called Legendre
decomposition, which factorizes an input tensor into a multiplicative
combination of parameters. Thanks to the well-developed theory of information
geometry, the reconstructed tensor is unique and always minimizes the KL
divergence from an input tensor. We empirically show that Legendre
decomposition can more accurately reconstruct tensors than other nonnegative
tensor decomposition methods.
