Sequential unfolding SVD for tensors with applications in array signal processing
IEEE Transactions on Signal Processing
Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar
IEEE Transactions on Signal Processing
SIAM Journal on Matrix Analysis and Applications
Block component analysis, a new concept for blind source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
On computation of approximate joint block-diagonalization using ordinary AJD
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
A combination of parallel factor and independent component analysis
Signal Processing
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications
Computational Statistics & Data Analysis
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In this paper we introduce a new class of tensor decompositions. Intuitively, we decompose a given tensor block into blocks of smaller size, where the size is characterized by a set of mode-$n$ ranks. We study different types of such decompositions. For each type we derive conditions under which essential uniqueness is guaranteed. The parallel factor decomposition and Tucker's decomposition can be considered as special cases in the new framework. The paper sheds new light on fundamental aspects of tensor algebra.