Multimodal data fusion: an overview of methods, challenges, and prospects
In various disciplines, information about the same phenomenon can be acquired from
different types of detectors, at different conditions, in multiple experiments or subjects …
different types of detectors, at different conditions, in multiple experiments or subjects …
Multimodal fusion of brain imaging data: a key to finding the missing link (s) in complex mental illness
It is becoming increasingly clear that combining multimodal brain imaging data provides
more information for individual subjects by exploiting the rich multimodal information that …
more information for individual subjects by exploiting the rich multimodal information that …
Tensor decompositions for signal processing applications: From two-way to multiway component analysis
The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move …
highlighted the limitations of standard flat-view matrix models and the necessity to move …
Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes
Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …
received considerable attention in previous decades. Currently, a plant-wide process …
Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition
Information fusion is a key step in multimodal biometric systems. The fusion of information
can occur at different levels of a recognition system, ie, at the feature level, matching-score …
can occur at different levels of a recognition system, ie, at the feature level, matching-score …
Removal of movement-induced EEG artifacts: current state of the art and guidelines
Objective: Electroencephalography (EEG) is a non-invasive technique used to record
cortical neurons' electrical activity using electrodes placed on the scalp. It has become a …
cortical neurons' electrical activity using electrodes placed on the scalp. It has become a …
Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis
Canonical correlation analysis (CCA) has been one of the most popular methods for
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …
A survey on canonical correlation analysis
In recent years, the advances in data collection and statistical analysis promotes canonical
correlation analysis (CCA) available for more advanced research. CCA is the main …
correlation analysis (CCA) available for more advanced research. CCA is the main …
A review of multivariate methods for multimodal fusion of brain imaging data
The development of various neuroimaging techniques is rapidly improving the
measurements of brain function/structure. However, despite improvements in individual …
measurements of brain function/structure. However, despite improvements in individual …
Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning
Cross-company defect prediction (CCDP) learns a prediction model by using training data
from one or multiple projects of a source company and then applies the model to the target …
from one or multiple projects of a source company and then applies the model to the target …