Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification

[1]  Xiaofeng Zhu,et al.  Unsupervised feature selection by self-paced learning regularization , 2020, Pattern Recognit. Lett..

[2]  Xiaofeng Zhu,et al.  One-Step Multi-View Spectral Clustering , 2019, IEEE Transactions on Knowledge and Data Engineering.

[3]  Shichao Zhang,et al.  Low-Rank Sparse Subspace for Spectral Clustering , 2019, IEEE Transactions on Knowledge and Data Engineering.

[4]  Xuelong Li,et al.  Describing Video With Attention-Based Bidirectional LSTM , 2019, IEEE Transactions on Cybernetics.

[5]  Xuelong Li,et al.  From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Jian Weng,et al.  Feature selection for text classification: A review , 2018, Multimedia Tools and Applications.

[7]  Heng Tao Shen,et al.  Video Captioning by Adversarial LSTM , 2018, IEEE Transactions on Image Processing.

[8]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Xiaoshuai Sun,et al.  Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length , 2018, IEEE Transactions on Multimedia.

[10]  Xiaofeng Zhu,et al.  Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection , 2018, IEEE Transactions on Knowledge and Data Engineering.

[11]  Meng Wang,et al.  Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder , 2018, IEEE Transactions on Image Processing.

[12]  Xiaofeng Zhu,et al.  Dynamic graph learning for spectral feature selection , 2018, Multimedia Tools and Applications.

[13]  Xiaofeng Zhu,et al.  Unsupervised feature selection via local structure learning and sparse learning , 2017, Multimedia Tools and Applications.

[14]  Dinggang Shen,et al.  Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers , 2017, IEEE Transactions on Big Data.

[15]  Heng Tao Shen,et al.  Video Captioning With Attention-Based LSTM and Semantic Consistency , 2017, IEEE Transactions on Multimedia.

[16]  Shichao Zhang,et al.  Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Xuelong Li,et al.  Graph PCA Hashing for Similarity Search , 2017, IEEE Transactions on Multimedia.

[18]  Dinggang Shen,et al.  A novel relational regularization feature selection method for joint regression and classification in AD diagnosis , 2017, Medical Image Anal..

[19]  Xiaofeng Zhu,et al.  Graph self-representation method for unsupervised feature selection , 2017, Neurocomputing.

[20]  S. Lahmiri Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance , 2016 .

[21]  Dinggang Shen,et al.  Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification , 2016, IEEE Transactions on Biomedical Engineering.

[22]  Xuelong Li,et al.  Block-Row Sparse Multiview Multilabel Learning for Image Classification , 2016, IEEE Transactions on Cybernetics.

[23]  Heikki Huttunen,et al.  Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects , 2015, NeuroImage.

[24]  Shuicheng Yan,et al.  Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization , 2014, IEEE Transactions on Image Processing.

[25]  Xiaofeng Zhu,et al.  A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis , 2014, NeuroImage.

[26]  Zi Huang,et al.  A Sparse Embedding and Least Variance Encoding Approach to Hashing , 2014, IEEE Transactions on Image Processing.

[27]  Zongming Ma,et al.  Adaptive Sparse Reduced-rank Regression , 2014 .

[28]  Dinggang Shen,et al.  Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification , 2014, NeuroImage.

[29]  Yong Hu,et al.  Brain resting-state functional MRI connectivity: Morphological foundation and plasticity , 2014, NeuroImage.

[30]  Salim Lahmiri,et al.  New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images. , 2014, Healthcare technology letters.

[31]  L. Rohde,et al.  Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction , 2012, Front. Syst. Neurosci..

[32]  Peter A. Bandettini,et al.  Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images , 2012, NeuroImage.

[33]  Vince D. Calhoun,et al.  A review of multivariate methods for multimodal fusion of brain imaging data , 2012, Journal of Neuroscience Methods.

[34]  Daoqiang Zhang,et al.  Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease , 2012, NeuroImage.

[35]  Shannon L. Risacher,et al.  Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression , 2011, MICCAI.

[36]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[37]  Huiru Zheng,et al.  Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[38]  Christos Davatzikos,et al.  Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI , 2009, NeuroImage.

[39]  Murray A. Jorgensen Iteratively Reweighted Least Squares , 2006 .

[40]  Aleix M. Martínez,et al.  Subclass discriminant analysis , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Karl J. Friston,et al.  Identification of degenerate neuronal systems based on intersubject variability , 2006, NeuroImage.

[42]  Angelo Cappello,et al.  Identification of distinct characteristics of postural sway in Parkinson's disease: A feature selection procedure based on principal component analysis , 2006, Neuroscience Letters.

[43]  Snigdhansu Chatterjee,et al.  Procrustes Problems , 2005, Technometrics.

[44]  J. Edward Jackson Reduced Rank Regression , 2005 .

[45]  J. Baron,et al.  FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment , 2005, Neurocase.

[46]  Nick C Fox,et al.  Imaging cerebral atrophy: normal ageing to Alzheimer's disease , 2004, The Lancet.

[47]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[48]  Dong Soo Lee,et al.  Quantification of F-18 FDG PET Images in Temporal Lobe Epilepsy Patients Using Probabilistic Brain Atlas , 2000, NeuroImage.

[49]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[50]  G. Reinsel,et al.  Multivariate Reduced-Rank Regression: Theory and Applications , 1998 .

[51]  Alan C. Evans,et al.  3D Anatomical Atlas of the Human Brain , 1998, NeuroImage.

[52]  G. Reinsel,et al.  Applications of Reduced-Rank Regression in Financial Economics , 1998 .

[53]  A. Izenman Reduced-rank regression for the multivariate linear model , 1975 .

[54]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.