no code implementations • 7 Aug 2023 • Cesar F. Caiafa, Ramiro M. Irastorza
Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies.
no code implementations • 22 Jun 2021 • Jin Zhang, Fan Feng, Pere Marti-Puig, Cesar F. Caiafa, Zhe Sun, Feng Duan, Jordi Solé-Casals
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.
1 code implementation • 28 Nov 2020 • Cesar F. Caiafa, Ziyao Wang, Jordi Solé-Casals, Qibin Zhao
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.
1 code implementation • NeurIPS 2019 • Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
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.
no code implementations • 25 Sep 2019 • Cesar F. Caiafa, Ziyao Wang, Jordi Solé-Casals, Qibin Zhao
This paper addresses the problem of training a classifier on incomplete data and its application to a complete or incomplete test dataset.
no code implementations • 13 Jun 2018 • Jordi Sole-Casals, Cesar F. Caiafa, Qibin Zhao, Adrzej Cichocki
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.
no code implementations • NeurIPS 2017 • Cesar F. Caiafa, Olaf Sporns, Andrew Saykin, Franco Pestilli
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.
2 code implementations • 27 May 2015 • Cesar F. Caiafa, Franco Pestilli
The number of neuroimaging data sets publicly available is growing at fast rate.
1 code implementation • 5 Jul 2012 • Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Zenas C. Chao, Yasuo Nagasaka, Naotaka Fujii, Liqing Zhang, Andrzej Cichocki
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.