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Found 8 papers, 1 papers with code
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria
Based on this idea, in previous works array recording systems and sensor selection strategies based on the Mutual Information (MI) criterion have been developed.
A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection
This system provides several constraints and assumptions on the data properties that might be not valid for multimodal data analysis, especially when large scale datasets collected from heterogeneous sources are considered, so that the accuracy and robustness of the outcomes might be severely jeopardized.
Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction
The nonstationarity of the source signals can be used as a discriminative property for signal separation.
A Hypothesis Testing Approach to Nonstationary Source Separation
The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem.
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction
Nonetheless, when applied to multimodal datasets (i. e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations.
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.
Dynamical spectral unmixing of multitemporal hyperspectral images
In this paper, we consider the problem of unmixing a time series of hyperspectral images.
On the Achievability of Cramér-Rao Bound In Noisy Compressed Sensing
Recently, it has been proved in Babadi et al. that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramer-Rao lower bound of the problem. To prove this result, this paper used a lemma, which is provided in Akcakaya et al, that comprises the main building block of the proof.
