Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

doi:10.1038/srep41069

Cited by 49 publications

(45 citation statements)

References 48 publications

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“…Inspired by [18, 13], the HIV specific pattern, identified in the Selection Step during training, is defined by the optimal ‘weight’ vector specifying a linear multivariate model defined by image measurements that correctly label subjects according to the MKL model. MKL classifies samples by learning the optimal pairings between kernels and image measurements.…”

Section: Methodsmentioning

confidence: 99%

“…The optimal ‘weight’ vector now depends on the weight C of the term measuring classification accuracy and the weight λ of the term measuring group sparsity within the MKL cost function (refer to Appendix A for more details). As in [13, 53], the search space for those two hyperparameters is {10 −3 , 10 −2 , 10 −1 , 1, 10 1 , 10 2 , 10 3 }. To identify the best hyperparameter setting, we perform 5-fold inner cross-validation 10 times.…”

Section: Methodsmentioning

confidence: 99%

“…By contrast, our proposed analysis is a type of machine learning framework that considers all image measurements together to identify the subset of measurements (called patterns) specific to HIV and then relates the significance of the pattern to its accuracy in distinguishing individuals with HIV from CTRLs. A popular approach for identifying patterns uses sparse classifiers [12, 13, 14, 15, 16, 8], which assume that only a few measurements are informative for distinguishing cohorts. After identifying a pattern, the corresponding measures are often applied to a second (non-sparse) approach, which focuses only on improving classification accuracy [17, 18, 19, 20, 21, 22].…”

Section: Introductionmentioning

confidence: 99%

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Chained regularization for identifying brain patterns specific to HIV infection

et al. 2018

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Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires a better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (ℓ-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (ℓ-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chained regularization for identifying brain patterns specific to HIV infection

et al. 2018

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“…Moreover, the data heterogeneity across the modalities (modality heterogeneity for short) is also ignored in their formulations. On the other hand, though the advanced machine learning method such as Multiple Kernel Learning (MKL), is able to model the complex data-to-label relationship of heterogeneous multimodality data [2,7,13], it is currently only applicable to the set of complete data.…”

Section: Introductionmentioning

confidence: 99%

Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data

Zhu¹,

Thung²,

Adeli³

et al. 2017

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

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It is challenging to use incomplete multimodality data for Alzheimer’s Disease (AD) diagnosis. The current methods to address this challenge, such as low-rank matrix completion (i.e., imputing the missing values and unknown labels simultaneously) and multi-task learning (i.e., defining one regression task for each combination of modalities and then learning them jointly), are unable to model the complex data-to-label relationship in AD diagnosis and also ignore the heterogeneity among the modalities. In light of this, we propose a new Maximum Mean Discrepancy (MMD) based Multiple Kernel Learning (MKL) method for AD diagnosis using incomplete multimodality data. Specifically, we map all the samples from different modalities into a Reproducing Kernel Hilbert Space (RKHS), by devising a new MMD algorithm. The proposed MMD method incorporates data distribution matching, pair-wise sample matching and feature selection in an unified formulation, thus alleviating the modality heterogeneity issue and making all the samples comparable to share a common classifier in the RKHS. The resulting classifier obviously captures the nonlinear data-to-label relationship. We have tested our method using MRI and PET data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset for AD diagnosis. The experimental results show that our method outperforms other methods.

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“…Nevertheless, they face a deficiency, namely, the feature spaces extracted from EEG signals can induce significant and complex variations regarding the nonlinearity and sparsity of the manifolds that hardly can be encoded by linear decompositions (Sturm et al, 2016 ). Moreover, based on their contribution to a linear regression model, linear dimensionality reduction methods usually select the most compact and relevant set of features, which might not be the best option for a non-linear classifier (Adeli et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

Álvarez-Meza

Gutiérrez

Castellanos-Domínguez

2017

Front. Neurosci.

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We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.

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Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Cited by 49 publications

References 48 publications

Chained regularization for identifying brain patterns specific to HIV infection

Chained regularization for identifying brain patterns specific to HIV infection

Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

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