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Found 6 papers, 2 papers with code
An AO-ADMM approach to constraining PARAFAC2 on all modes
We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted patterns.
Concurrent Alternating Least Squares for multiple simultaneous Canonical Polyadic Decompositions
We observe that, in practice, experts often have to compute multiple decompositions of the same tensor, each with a small number of components (typically fewer than 20), to ultimately find the best ones to use for the application at hand.
Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring
There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks.
Cross-product Penalized Component Analysis (XCAN)
In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation.
Nonnegative PARAFAC2: a flexible coupling approach
In the following manuscript, a relaxation of the PARAFAC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode.
Joint Tensor Factorization and Outlying Slab Suppression with Applications
Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants.
