ArticleIEEE transactions on pattern analysis and machine intelligence2019
Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.
Article in IEEE transactions on pattern analysis and machine intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions.Frontiers in aging neuroscience · 2023Review
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Adversarial concept drift detection under poisoning attacks for robust data stream mining.Machine learning · 2022Article
- Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.Applied soft computing · 2022Article
- A Deep-Learning Based Visual Sensing Concept for a Robust Classification of Document Images under Real-World Hard Conditions.Sensors (Basel, Switzerland) · 2021Article
- Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection.Computational intelligence and neuroscience · 2021Review
- Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection.IEEE transactions on pattern analysis and machine intelligence · 2020Article
- Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.IEEE transactions on pattern analysis and machine intelligence · 2020Article
- Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research.Frontiers in psychiatry · 2020Review
- A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.Frontiers in neuroscience · 2020Review
- Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease.International journal of molecular sciences · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson's and Alzheimer's diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.
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