A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
TensorTextures: multilinear image-based rendering
ACM SIGGRAPH 2004 Papers
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Out-of-core tensor approximation of multi-dimensional matrices of visual data
ACM SIGGRAPH 2005 Papers
A statistical framework for genomic data fusion
Bioinformatics
Generalized LARS as an effective feature selection tool for text classification with SVMs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Generalized Low Rank Approximations of Matrices
Machine Learning
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Cognitive Neuroscience
Applying spatial distribution analysis techniques to classification of 3D medical images
Artificial Intelligence in Medicine
Learning subspace kernels for classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Multi-modal multi-task learning for joint prediction of clinical scores in Alzheimer's disease
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Predicting clinical scores using semi-supervised multimodal relevance vector regression
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
An integrated data mining approach to real-time clinical monitoring and deterioration warning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-source learning with block-wise missing data for Alzheimer's disease prediction
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.