The Geometry of Rank-One Tensor Completion

The geometry of the set of restrictions of rank-one tensors to some of their coordinates is studied. This gives insight into the problem of rank-one completion of partial tensors. Particular emphasis is put on the semialgebraic nature of the problem, which arises for real tensors with constraints on the parameters. The algebraic boundary of the completable region is described for tensors parametrized by probability distributions and where the number of observed entries equals the number of parameters. If the observations are on the diagonal of a tensor of format $d\times\dots\times d$, the complete semialgebraic description of the completable region is found.

[1]  R. Bro,et al.  Practical aspects of PARAFAC modeling of fluorescence excitation‐emission data , 2003 .

[2]  Franz J. Király,et al.  Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion , 2013, NIPS.

[3]  Rafael H. Villarreal,et al.  Rees algebras of edge ideals , 1995 .

[4]  Yaoliang Yu,et al.  Approximate Low-Rank Tensor Learning , 2014 .

[5]  Zvi Rosen,et al.  Matrix Completion for the Independence Model , 2014, 1407.3254.

[6]  Pierre Comon,et al.  Blind Multilinear Identification , 2012, IEEE Transactions on Information Theory.

[7]  T. Willmore Algebraic Geometry , 1973, Nature.

[8]  B. Sturmfels Gröbner bases and convex polytopes , 1995 .

[9]  Ming Yuan,et al.  On Tensor Completion via Nuclear Norm Minimization , 2014, Foundations of Computational Mathematics.

[10]  Stephen P. Boyd,et al.  A rank minimization heuristic with application to minimum order system approximation , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[11]  J. Kruskal Rank, decomposition, and uniqueness for 3-way and n -way arrays , 1989 .

[12]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..

[13]  B. Recht,et al.  Tensor completion and low-n-rank tensor recovery via convex optimization , 2011 .

[14]  B. Sturmfels,et al.  Binomial Ideals , 1994, alg-geom/9401001.

[15]  Takayuki Hibi,et al.  Toric Ideals Generated by Quadratic Binomials , 1999 .

[16]  Pablo A. Parrilo,et al.  The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.

[17]  F. Király,et al.  Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion , 2013 .

[18]  Dinesh Manocha,et al.  SOLVING SYSTEMS OF POLYNOMIAL EQUATIONS , 2002 .

[19]  Tamara G. Kolda,et al.  Scalable Tensor Factorizations for Incomplete Data , 2010, ArXiv.

[20]  Cynthia Vinzant,et al.  Determinantal representations of hyperbolic plane curves: An elementary approach , 2012, J. Symb. Comput..

[21]  E. Davidson,et al.  Strategies for analyzing data from video fluorometric monitoring of liquid chromatographic effluents , 1981 .

[22]  David A. Cox,et al.  Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 3/e (Undergraduate Texts in Mathematics) , 2007 .

[23]  Johan A. K. Suykens,et al.  A Rank-One Tensor Updating Algorithm for Tensor Completion , 2015, IEEE Signal Processing Letters.

[24]  Sonja Petrović,et al.  Toric algebra of hypergraphs , 2012, 1206.1904.

[25]  Leiba Rodman,et al.  Ranks of Completions of Partial Matrices , 1989 .

[26]  WonkaPeter,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2013 .

[27]  Louis Theran,et al.  Algebraic Matroids with Graph Symmetry , 2013, 1312.3777.

[28]  Franz J. Király,et al.  The algebraic combinatorial approach for low-rank matrix completion , 2012, J. Mach. Learn. Res..

[29]  Rainer Sinn Algebraic Boundaries of $$\mathrm{SO}(2)$$SO(2)-Orbitopes , 2013, Discret. Comput. Geom..

[30]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  J. A. Ward,et al.  Rank-one completions of partial matrices and completely rank-nonincreasing linear functionals , 2006 .

[32]  Christopher J. Hillar,et al.  Most Tensor Problems Are NP-Hard , 2009, JACM.

[33]  Emmanuel J. Candès,et al.  The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.

[34]  Baoxin Li,et al.  Tensor completion for on-board compression of hyperspectral images , 2010, 2010 IEEE International Conference on Image Processing.

[35]  Amit Singer,et al.  Uniqueness of Low-Rank Matrix Completion by Rigidity Theory , 2009, SIAM J. Matrix Anal. Appl..

[36]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[37]  Bo Huang,et al.  Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery , 2013, ICML.