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 19 articles )

Showing results 1 to 19 of 19.
Sorted by year (citations)

  1. Filipović, Marko; Jukić, Ante: Tucker factorization with missing data with application to low-$n$-rank tensor completion (2015) ioport
  2. Zhang, Min; Yang, Lei; Huang, Zheng-Hai: Minimum $ n$-rank approximation via iterative hard thresholding (2015)
  3. Kressner, Daniel; Tobler, Christine: Algorithm 941: htucker -- a Matlab toolbox for tensors in hierarchical Tucker format (2014)
  4. Grasedyck, Lars; Kressner, Daniel; Tobler, Christine: A literature survey of low-rank tensor approximation techniques (2013)
  5. Zander, Elmar K.: Tensor approximation methods for stochastic problems (2013)
  6. Liu, Ji; Liu, Jun; Wonka, Peter; Ye, Jieping: Sparse non-negative tensor factorization using columnwise coordinate descent (2012)
  7. Unkel, Steffen; Hannachi, Abdel; Trendafilov, Nickolay T.; Jolliffe, Ian T.: Independent component analysis for three-way data with an application from atmospheric science (2011)
  8. Kolda, Tamara G.; Bader, Brett W.: Tensor decompositions and applications (2009)
  9. Li, Guo-Zheng; Meng, Hao-Hua; Yang, Mary Qu; Yang, Jack Y.: Combining support vector regression with feature selection for multivariate calibration (2009) ioport
  10. Martínez-Montes, Eduardo; Sánchez-Bornot, José M.; Valdés-Sosa, Pedro A.: Penalized PARAFAC analysis of spontaneous EEG recordings (2008)
  11. Moravitz Martin, Carla D.; Van Loan, Charles F.: A Jacobi-type method for computing orthogonal tensor decompositions (2008)
  12. Mørup, Morten; Hansen, Lars Kai; Arnfred, Sidse M.: Algorithms for sparse nonnegative Tucker decompositions (2008)
  13. Rajih, Myriam; Comon, Pierre; Harshman, Richard A.: Enhanced line search: a novel method to accelerate PARAFAC (2008)
  14. Bader, Brett W.; Kolda, Tamara G.: Efficient MATLAB computations with sparse and factored tensors (2007)
  15. Kunz, Werner: Visualization of competitive market structure by means of choice data (2007)
  16. Skillicorn, David: Understanding complex datasets. Data mining with matrix decompositions (2007)
  17. Bader, Brett W.; Kolda, Tamara G.: Algorithm 862: MATLAB tensor classes for fast algorithm prototyping. (2006)
  18. Tomasi, Giorgio; Bro, Rasmus: A comparison of algorithms for Fitting the PARAFAC model (2006)
  19. Lacy, Seth L.; Bernstein, Dennis S.: Identification of FIR Wiener systems with unknown, non-invertible, polynomial non-linearities (2003)