Higher-Order SVD-Based Subspace Estimation to Improve the Parameter Estimation Accuracy in Multidimensional Harmonic Retrieval Problems

By Martin Haardt, Florian Roemer, Giovanni Del Galdo. IEEE Transactions on Signal Processing, 2008

378 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/192b62ca1d98616daa20409e4923cbc7f12cbce8

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In Signal Processing

Citations in the corpus

A New Truncation Strategy for the Higher-Order Singular Value Decomposition (2012, methodology): “The HOSVD has been applied in numerous application domains [29], such as image processing [9, 32, 39, 61], pattern recognition [49, 50, 59, 60, 62], data mining [33, 34, 52, 53], signal processing [12, 19, 36, 37, 38, 45], psychometrics [54, 55, 56], chemometrics [5], and biomedicine [16, 40, 41].”
Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme (2011, background): “The main applications of the tensor approximation discussed in this paper are the dimensionality reduction of tensors with high dimensions [14, 4, 15, 16, 27, 42, 26] and signal subspace estimation [36, 37, 27, 42, 26, 19].”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, methodology): “We have shown that Tucker decomposition may be considered as a multilinear extension of PCA [8]; it therefore generalizes signal subspace techniques, with applications including classification, feature extraction, and subspace-based harmonic retrieval [25, 39, 75, 76].”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, positive): “We have shown that the Tucker/HOSVD decomposition may be considered as a multilinear extension of PCA [160]; it therefore generalizes signal subspace techniques and finds application in areas including multilinear blind source separation, classification, feature extraction, and subspace-based harmon”
Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar (2010, methodology): “By extension, the PARAFAC decomposition of an th-order tensor in which all loading matrices have a Vandermonde structure is an -”
On the best low multilinear rank approximation of higher-order tensors (2010, methodology): “The best low multilinear rank approximation of tensors is used for signal subspace estimation [60, 61, 52, 67, 51, 35 ] and as a dimensionality reduction tool for tensors with high dimensions [27, 4, 29, 30, 52, 67, 51], including simultaneous dimensionality reduction of a matrix and a tensor [27].”