N-way Toolbox

The N-way toolbox for MATLAB. The N-way toolbox provides means for: Fitting multi-way PARAFAC models; Fitting multi-way PLS regression models; Fitting multi-way Tucker models; Fitting the generalized rank annihilation method; Fitting the direct trilinear decomposition; Fitting models subject to constraints on the parameters such as e.g. nonnegativity, unimodality, orthogonality; Fitting models with missing values (using expectation maximization); Fitting models with a weighted least squares loss function (including MILES); Predicting scores for new samples using a given model; Predicting the dependent variable(s) of PLS models; Performing multi-way scaling and centering; Performing cross-validation of models; Calculating core consistency of PARAFAC models; Using additional diagnostic tools to evaluate the appropriate number of components; Perform rotations of core and models in Tucker models; Plus additional utility functions. In addition to the N-way toolbox, you can find a number of other multi-way tools on this site including PARAFAC2, Slicing (for exponential data such as low-res NMR), GEMANOVA for generalized multiplicative ANOVA, MILES for maximum likelihood fitting, conload for congruence and correlation loadings, eemscat for scatter handling of EEM data, clustering for multi-way clustering, CuBatch for batch data analysis, indafac for PARAFAC, PARALIND for constrained PARAFAC models, jackknifing for PARAFAC.


References in zbMATH (referenced in 29 articles )

Showing results 1 to 20 of 29.
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  1. Usevich, Konstantin; Dreesen, Philippe; Ishteva, Mariya: Decoupling multivariate polynomials: interconnections between tensorizations (2020)
  2. Alexandrov, Boian S.; Stanev, Valentin G.; Vesselinov, Velimir V.; Rasmussen, Kim Ø.: Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers (2019)
  3. Kaya, Oguz; Robert, Yves: Computing dense tensor decompositions with optimal dimension trees (2019)
  4. Kaya, Oguz; Uçar, Bora: Parallel Candecomp/Parafac decomposition of sparse tensors using dimension trees (2018)
  5. Kim, Mijung; Candan, K. Selçuk: Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient CP and Tucker decompositions (2016)
  6. Tortora, Cristina; Summa, Mireille Gettler; Marino, Marina; Palumbo, Francesco: Factor probabilistic distance clustering (FPDC): a new clustering method (2016)
  7. Filipović, Marko; Jukić, Ante: Tucker factorization with missing data with application to low-(n)-rank tensor completion (2015)
  8. Modesto, David; Zlotnik, Sergio; Huerta, Antonio: Proper generalized decomposition for parameterized Helmholtz problems in heterogeneous and unbounded domains: application to harbor agitation (2015)
  9. Zhang, Min; Yang, Lei; Huang, Zheng-Hai: Minimum ( n)-rank approximation via iterative hard thresholding (2015)
  10. Hackbusch, Wolfgang: Numerical tensor calculus (2014)
  11. Kressner, Daniel; Tobler, Christine: Algorithm 941: \texttthtucker-- a Matlab toolbox for tensors in hierarchical Tucker format (2014)
  12. Tan, Huachun; Cheng, Bin; Feng, Jianshuai; Liu, Li; Wang, Wuhong: Mixture augmented Lagrange multiplier method for tensor recovery and its applications (2014)
  13. Grasedyck, Lars; Kressner, Daniel; Tobler, Christine: A literature survey of low-rank tensor approximation techniques (2013)
  14. Martens, Harald; Tøndel, Kristin; Tafintseva, Valeriya; Kohler, Achim; Plahte, Erik; Vik, Jon Olav; Gjuvsland, Arne B.; Omholt, Stig W.: PLS-based multivariate metamodeling of dynamic systems (2013)
  15. Zander, Elmar K.: Tensor approximation methods for stochastic problems (2013)
  16. Liu, Ji; Liu, Jun; Wonka, Peter; Ye, Jieping: Sparse non-negative tensor factorization using columnwise coordinate descent (2012)
  17. Unkel, Steffen; Hannachi, Abdel; Trendafilov, Nickolay T.; Jolliffe, Ian T.: Independent component analysis for three-way data with an application from atmospheric science (2011)
  18. Kolda, Tamara G.; Bader, Brett W.: Tensor decompositions and applications (2009)
  19. Li, Guo-Zheng; Meng, Hao-Hua; Yang, Mary Qu; Yang, Jack Y.: Combining support vector regression with feature selection for multivariate calibration (2009) ioport
  20. Martínez-Montes, Eduardo; Sánchez-Bornot, José M.; Valdés-Sosa, Pedro A.: Penalized PARAFAC analysis of spontaneous EEG recordings (2008)

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