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Found 3 papers, 1 papers with code
TensorProjection Layer: A Tensor-Based Dimensionality Reduction Method in CNN
In this paper, we propose a dimensionality reduction method applied to tensor-structured data as a hidden layer (we call it TensorProjection Layer) in a convolutional neural network.
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy
The first stage treats the image as a matrix, which is a tensor of order 2, and uses multilinear principal component analysis (MPCA) for matrix rank reduction and image denoising.
Functional Inverse Regression in an Enlarged Dimension Reduction Space
Our operator and functional analysis based approach facilitates a compact and rigorous formulation of the functional inverse regression problem.
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