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Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso
TLDR
A multi-task sparse group lasso (MT-SGL) framework, which estimates sparse features coupled across tasks, and can work with loss functions associated with any Generalized Linear Models, is presented.
Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease
TLDR
The experimental results with real and synthetic data demonstrate that incorporating the two prior structures by the generalized fused group lasso norm into the multi task feature learning can improve the prediction performance over several state-of-the-art competing methods.
A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD
TLDR
The proposed ℓ2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance.
A multi-kernel based framework for heterogeneous feature selection and over-sampling for computer-aided detection of pulmonary nodules
TLDR
A unified multiple kernel framework to classify potential nodule objects is proposed, involving multiple kernel learning with a ź 2 , 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and a multi-kernel over-sampling for the imbalanced data learning.
Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.
TLDR
Overampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples in Breast CAD are proposed.
ℓ2, 1 Norm Regularized Multi-kernel Based Joint Nonlinear Feature Selection and Over-sampling for Imbalanced Data Classification
TLDR
The experimental results demonstrate that jointly operating nonlinear feature selection and oversampling with 2,1 norm multi-kernel learning framework (2,1 MKFSOS) can lead to a promising classification performance.
Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures
TLDR
The experimental results not only demonstrate the proposed multi-kernel based dimensionality reduction and over-sampling method has superior performance over multiple comparable methods, but also identifies relevant imaging biomarkers that are consistent with prior medical knowledge.
Cost Sensitive Ranking Support Vector Machine for Multi-label Data Learning
TLDR
A novel solution, called CSRankSVM (Cost sensitive Ranking Support Vector Machine), which assigns a different misclassification cost for each labelset to effectively tackle the problem of imbalance for Multi-label data is proposed.
ℓ2, 1-ℓ1 regularized nonlinear multi-task representation learning based cognitive performance prediction of Alzheimer's disease
TLDR
The proposed methods achieve not only clearly improved prediction performance for cognitive measurements with single MRI modality or multi-modalities data, but also a compact set of highly suggestive biomarkers relevant to AD.
Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer’s Disease
TLDR
The experimental results demonstrate that incorporating the two prior structures with fused group lasso norm into the multi-task feature learning can improve prediction performance over several competing methods, with estimated correlations of cognitive functions and identification of cognition-relevant imaging markers that are clinically and biologically meaningful.
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