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Found 18 papers, 0 papers with code
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)
Due to this fact, in this article, we propose the imbalanced Temporal Deep Gaussian Process (iTDGP), a probabilistic model that can improve AIS lesions prediction by using baseline CTP time series.
Shared Space Transfer Learning for analyzing multi-site fMRI data
The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space.
Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset
This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality -- such as whole-brain images.
Supervised Hyperalignment for multi-subject fMRI data alignment
This paper proposes a Supervised Hyperalignment (SHA) method to ensure better functional alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli.
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data
Classical RSA techniques employ the inverse of the covariance matrix to explore a linear model between the neural activities and task events.
Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns.
Gradient Hyperalignment for multi-subject fMRI data alignment
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies.
Deep Hyperalignment
This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI datasets with nonlinearity, high-dimensionality (broad ROI), and a large number of subjects.
Anatomical Pattern Analysis for decoding visual stimuli in human brains
Methods: In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain.
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains
There is a wide range of challenges in the MVP techniques, i. e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and the cost of brain studies.
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory
We employ four conditions in the WOC theory, i. e., diversity, independency, decentralization and aggregation, to guide both the constructing of individual clustering results and the final combination for clustering ensemble.
Local Discriminant Hyperalignment for multi-subject fMRI data alignment
Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks.
A new selection strategy for selective cluster ensemble based on Diversity and Independency
In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection.
Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images
In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain.
Wisdom of Crowds cluster ensemble
These include decentralization criteria for generating primary results, independence criteria for the base algorithms, and diversity criteria for the ensemble members.
A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding
Moreover, by testing a number of different features, this paper found that we can reduce the additional cost in feature selection stage by using this method.
Evaluating the effect of topic consideration in identifying communities of rating-based social networks
Finding meaningful communities in social network has attracted the attentions of many researchers.
Weighted Spectral Cluster Ensemble
Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increasing the performance of the final results.
