Multi-task Feature Selection Research Articles

Joint regression and classification via relational regularization for Parkinson's disease diagnosis.

It is known that the symptoms of Parkinson’s disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson’s Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis.

Multi-Label Nonlinear Matrix Completion With Transductive Multi-Task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient With High-Grade Gliomas.

The O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase 1 (IDH1) mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which has limited their wider clinical implementation. Accurate presurgical prediction of their statuses based on preoperative multimodal neuroimaging is of great clinical value for a better treatment plan. Currently, the available data set associated with this study has several challenges, such as small sample size and complex, nonlinear (image) feature-to-(molecular) label relationship. To address these issues, we propose a novel multi-label nonlinear matrix completion (MNMC) model to jointly predict both MGMT and IDH1 statuses in a multi-task framework. Specifically, we first employ a nonlinear random Fourier feature mapping to improve the linear separability of the data, and then use transductive multi-task feature selection (performed in a nonlinearly transformed feature space) to refine the imputed soft labels, thus alleviating the overfitting problem caused by small sample size. We further design an optimization algorithm with a guaranteed convergence ability based on a block prox-linear method to solve the proposed MNMC model. Finally, by using a single-center, multimodal brain imaging and molecular pathology data set of HGG, we derive brain functional and structural connectomics features to jointly predict MGMT and IDH1 statuses. Results demonstrate that our proposed method outperforms the previously widely used single- and multi-task machine learning methods. This paper also shows the promise of utilizing brain connectomics for HGG prognosis in a non-invasive manner.

Multi-task learning for intelligent data processing in granular computing context

Classification is a popular task in many application areas, such as decision making, rating, sentiment analysis and pattern recognition. In the recent years, due to the vast and rapid increase in the size of data, classification has been mainly undertaken in the way of supervised machine learning. In this context, a classification task involves data labelling, feature extraction, feature selection and learning of classifiers. In traditional machine learning, data is usually single-labelled by experts, i.e., each instance is only assigned one class label, since experts assume that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in real applications. For example, in the context of emotion detection, there could be more than one emotion identified from the same person. On the other hand, feature selection has typically been done by evaluating feature subsets in terms of their relevance to all the classes. However, it is possible that a feature is only relevant to one class, but is irrelevant to all the other classes. Based on the above argumentation on data labelling and feature selection, we propose in this paper a framework of multi-task learning. In particular, we consider traditional machine learning to be single task learning, and argue the necessity to turn it into multi-task learning to allow an instance to belong to more than one class (i.e., multi-task classification) and to achieve class specific feature selection (i.e., multi-task feature selection). Moreover, we report two experimental studies in terms of fuzzy multi-task classification and rule learning based multi-task feature selection. The results show empirically that it is necessary to undertake multi-task learning for both classification and feature selection.

Least absolute deviations learning of multiple tasks

In this paper, we propose a new multitask feature selection model based on least absolute deviations. However, due to the inherent nonsmoothness of \begin{document}$l_1 $\end{document} norm, optimizing this model is challenging. To tackle this problem efficiently, we introduce an alternating iterative optimization algorithm. Moreover, under some mild conditions, its global convergence result could be established. Experimental results and comparison with the state-of-the-art algorithm SLEP show the efficiency and effectiveness of the proposed approach in solving multitask learning problems.

Multimodal Hyper-connectivity Networks for MCI Classification.

Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks.

Semi-supervised feature selection with exploiting shared information among multiple tasks

Given several related tasks, multi-task feature selection determines the importance of features by mining the correlations between them. There have already many efforts been made on the supervised multi-task feature selection. However, in real-world applications, it’s noticeably time-consuming and unpractical to collect sufficient labeled training data for each task. In this paper, we propose a novel feature selection algorithm, which integrates the semi-supervised learning and multi-task learning into a joint framework. Both the labeled and unlabeled samples are sufficiently utilized for each task, and the shared information between different tasks is simultaneously explored to facilitate decision making. Since the proposed objective function is non-smooth and difficult to be solved, we also design an efficient iterative algorithm to optimize it. Experimental results on different applications demonstrate the effectiveness of our algorithm.

Manifold regularized multi-view feature selection for social image annotation

The features used in many social media analysis-based applications are usually of very high dimension. Feature selection offers several advantages in highly dimensional cases. Recently, multi-task feature selection has attracted much attention, and has been shown to often outperform the traditional single-task feature selection. Current multi-task feature selection methods are either supervised or unsupervised. In this paper, we address the semi-supervised multi-task feature selection problem. We firstly introduce manifold regularization in multi-task feature selection to utilize the limited number of labeled samples and the relatively large amount of unlabeled samples. However, the graph constructed in manifold regularization from a single feature representation (view) may be unreliable. We thus propose to construct the graph using the heterogeneous feature representations from multiple views. The proposed method is called manifold regularized multi-view feature selection (MRMVFS), which can exploit the label information, label relationship, data distribution, as well as correlation among different kinds of features simultaneously to boost the feature selection performance. All these information are integrated into a unified learning framework to estimate feature selection matrix, as well as the adaptive view weights. Experimental results on three real-world image datasets, NUS-WIDE, Flickr and Animal, demonstrate the effectiveness and superiority of the proposed MRMVFS over other state-of-the-art feature selection methods.

Hyper-connectivity of functional networks for brain disease diagnosis.

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.

Multimodal learning for view-based 3D object classification

Nowadays by employing many machine learning and pattern classification methods in object classification, the view-based 3D object classification, an emerging research topic, becomes a major research focus. However, most existing researches focus on only a single modality of image features for the object classification, although recent studies have shown that different kinds of features may provide complementary information for 3D object classification. In this paper, we propose the multimodal support vector machine to combine three modalities of image features, i.e., Sift descriptor, Outline Fourier transform descriptor, and Zernike Moments descriptor to discriminate the multiple classes of object, where each kernel corresponds to each modality. In this way, not only the independence of each modality but also the interrelation between them are both taken into considered. And we further employ multi-task feature selection via the l2-norm regularization after feature extraction to improve the performance of final classification. The experiments conducted in ETH-80 image set demonstrate the effectiveness and superiority of our method.

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease

Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease

Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.

Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis.

Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder.

In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals.

An Effective Video Summarization Framework Toward Handheld Devices

Multivideo summarization aims to fuze multiple videos of sightseeing into a condensed, descriptive, and aesthetically pleasing one. It is a useful technique that can be applied for video displaying, storage, and communication. In this paper, we propose a new summarization technique that enforces video stability and preserves well-aesthetic frames. In particular, a multitask feature selection is introduced to efficiently discover the semantically important features. Then, the key frames are selected based on their contributions to reconstruct the video semantics. Thereafter, a probabilistic model is proposed to dynamically fit the key frames into an aesthetically pleasing video summary, wherein its constituent frames are adaptively destabilized. Experimental results on multiple videos taken from prominent sceneries throughout the world demonstrate the descriptiveness, aesthetics, and stability of the generated summary.

Inherent Structure-Guided Multi-view Learning for Alzheimer's Disease and Mild Cognitive Impairment Classification.

Multi-atlas based morphometric pattern analysis has been recently proposed for the automatic diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI), where multi-view feature representations for subjects are generated by using multiple atlases. However, existing multi-atlas based methods usually assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while the underlying distribution of data is actually a prior unknown. In this paper, we propose an inherent structure-guided multi-view leaning (ISML) method for AD/MCI classification. Specifically, we first extract multi-view features for subjects using multiple selected atlases, and then cluster subjects in the original classes into several sub-classes (i.e., clusters) in each atlas space. Then, we encode each subject with a new label vector, by considering both the original class labels and the coding vectors for those sub-classes, followed by a multi-task feature selection model in each of multi-atlas spaces. Finally, we learn multiple SVM classifiers based on the selected features, and fuse them together by an ensemble classification method. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves better performance than several state-of-the-art methods in AD/MCI classification.

Multi-subject brain decoding with multi-task feature selection.

In the neural science society, multi-subject brain decoding is of great interest. However, due to the variability of activation patterns across brains, it is difficult to build an effective decoder using fMRI samples pooled from different subjects. In this paper, a hierarchical model is proposed to extract robust features for decoding. With feature selection for each subject treated as a separate task, a novel multi-task feature selection method is introduced. This method utilizes both complementary information among subjects and local correlation between brain areas within a subject. Finally, using fMRI samples pooled from all subjects, a linear support vector machine (SVM) classifier is trained to predict 2-D stimuli-related images or 3-D stimuli-related images. The experimental results demonstrated the effectiveness of the proposed method.

Brain connectivity hyper-network for MCI classification.

Brain connectivity network has been used for diagnosis and classification of neurodegenerative diseases, such as Alzheimer's disease (AD) as well as its early stage, i.e., mild cognitive impairment (MCI). However, conventional connectivity network is usually constructed based on the pairwise correlation among brain regions and thus ignores the higher-order relationship among them. Such information loss is unexpected because the brain itself is a complex network and the higher-order interaction may contain useful information for classification. Accordingly, in this paper, we propose a new brain connectivity hyper-network based method for MCI classification. Here, the connectivity hyper-network denotes a network where an edge can connect more than two brain regions, which can be naturally represented with a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI time series using sparse representation modeling. Then, we extract three sets of the brain-region specific features from the connectivity hyper-networks, and exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results demonstrate the efficacy of our proposed method for MCI classification with comparison to the conventional connectivity network based methods.

Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification

Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification

Multi-task feature selection in microarray data by binary integer programming

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.