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Found 9 papers, 4 papers with code
A sparse coding approach to inverse problems with application to microwave tomography
Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies.
Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization
Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering.
Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding
A new supervised learning method is developed to train a general classifier, such as a logistic regression or a deep neural network, using only a subset of features per sample, while assuming sparse representations of data vectors on an unknown dictionary.
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
We develop an efficient optimization strategy for this extremely high-dimensional sparse problem, by reducing the number of parameters using a greedy algorithm designed specifically for the problem.
Supervised learning with incomplete data via sparse representations
This paper addresses the problem of training a classifier on incomplete data and its application to a complete or incomplete test dataset.
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery, however, not at the same level as clean data.
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms.
Sparse multiway decomposition for analysis and modeling of diffusion imaging and tractography
The number of neuroimaging data sets publicly available is growing at fast rate.
Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method
A new generalized multilinear regression model, termed the Higher-Order Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the data onto the latent space and performing regression on the corresponding latent variables.
