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Found 7 papers, 1 papers with code
Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)
We introduce PaLEnTIR, a significantly enhanced parametric level-set (PaLS) method addressing the restoration and reconstruction of piecewise constant objects.
Stable Tensor Neural Networks for Rapid Deep Learning
To exemplify the elegant, matrix-mimetic algebraic structure of our $t$-NNs, we expand on recent work (Haber and Ruthotto, 2017) which interprets deep neural networks as discretizations of non-linear differential equations and introduces stable neural networks which promote superior generalization.
Image classification using local tensor singular value decompositions
From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers.
Multilinear Subspace Clustering
In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images.
Clustering multi-way data: a novel algebraic approach
In this paper, we develop a method for unsupervised clustering of two-way (matrix) data by combining two recent innovations from different fields: the Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming from a union of subspaces into their respective subspaces, and the t-product [18], which was introduced to provide a matrix-like multiplication for third order tensors.
Novel methods for multilinear data completion and de-noising based on tensor-SVD
Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data.
Novel Factorization Strategies for Higher Order Tensors: Implications for Compression and Recovery of Multi-linear Data
In this paper we propose novel methods for compression and recovery of multilinear data under limited sampling.
