Alwin Stegeman

Computational Mathematics · 2002–2009

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computational Mathematics — 21 citations into it; #147 of 612 by trust here, #210 by citations
• Signal Processing — 17 citations into it; #2,144 of 7,572 by trust here, #4,201 by citations
• Computational Mechanics — 10 citations into it; #4,266 of 10,001 by trust here, #4,749 by citations
• Computational Theory and Mathematics — 9 citations into it; #5,658 of 12,182 by trust here, #11,462 by citations
• Computer Networks and Communications — 8 citations into it; #9,776 of 9,886 by trust here, #6,498 by citations
• Artificial Intelligence — 5 citations into it

Whom Alwin Stegeman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Lieven De Lathauwer
2. Joseph B. Kruskal
3. N. Sidiropoulos
4. P. Kroonenberg
5. Tamara G. Kolda
6. P. Paatero
7. Rasmus Bro
8. J. F. Berge
9. Vin de Silva
10. Brett W. Bader

Who trusts Alwin Stegeman overall

1. Lieven De Lathauwer
2. Andrzej S. Cichocki
3. Pierre Comon
4. Anh Huy Phan
5. Tamara G. Kolda
6. Danilo P. Mandic
7. Petr Tichavský
8. Guoxu Zhou
9. Brett W. Bader
10. Xavier Luciani

Papers from Alwin Stegeman's perspective

Ordered by how strongly the researchers Alwin Stegeman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Some mathematical notes on three-mode factor analysis
2. A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization (SIAM Journal on Matrix Analysis and Applications)
3. Tensor Decompositions and Applications
4. Applied Multiway Data Analysis
5. The Expression of a Tensor or a Polyadic as a Sum of Products
6. Parallel factor analysis in sensor array processing (IEEE Transactions on Signal Processing)
7. On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
8. Construction and analysis of degenerate PARAFAC models
9. Signal Processing based on Multilinear Algebra
10. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition

Their papers

• Subtracting a best rank-1 approximation may increase tensor rank (2009) — 66 citations
• On uniqueness conditions for Candecomp/Parafac and Indscal with full column rank in one mode (2009) — 61 citations
• Degeneracy in Candecomp/Parafac explained for p × p × 2 arrays of rank p + 1 or higher (2006) — 84 citations
• Is network traffic approximated by stable Levy motion or fractional Brownian motion (2002) — 316 citations
• On Kruskal's uniqueness condition for the Candecomp/Parafac decomposition (2007) — 258 citations