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Found 11 papers, 2 papers with code
Statistical and computational rates in high rank tensor estimation
We find that high-dimensional latent variable tensors are of log-rank; the fact explains the pervasiveness of low-rank tensors in applications.
Multiway Spherical Clustering via Degree-Corrected Tensor Block Models
We consider the problem of multiway clustering in the presence of unknown degree heterogeneity.
Smooth tensor estimation with unknown permutations
A phase transition phenomenon is revealed with respect to the smoothness threshold needed for optimal recovery.
Nonparametric Trace Regression in High Dimensions via Sign Series Representation
Trace regression is a widely used method to model effects of matrix predictors and has shown great success in matrix learning.
Beyond the Signs: Nonparametric Tensor Completion via Sign Series
A nonparametric approach to tensor completion is developed based on a new model which we coin as sign representable tensors.
Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc.
Tensor denoising and completion based on ordinal observations
Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies.
Supervised tensor decomposition with features on multiple modes
Here, we develop a tensor decomposition method that incorporates multiple feature matrices as side information.
Multiway clustering via tensor block models
We consider the problem of identifying multiway block structure from a large noisy tensor.
Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality
We consider the problem of decomposing a higher-order tensor with binary entries.
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)
Tensor decompositions have rich applications in statistics and machine learning, and developing efficient, accurate algorithms for the problem has received much attention recently.
