Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises

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.

Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI

“…This kind of methods typically suffers from the challenge of over-fitting, due to the very high (e.g., millions) dimensionality of features/voxels compared with the relatively small (e.g., tens or hundreds) number of subjects/images for model training. In contrast, region-based methods [12], [13], [14], [15], [16], [17], [18] extract quantitative features from pre-segmented brain regions to construct classifiers for identifying patients from normal controls (NCs). Intuitively, this kind of methods focuses only on empiricallydefined brain regions, and thus may fail to cover all possible pathological locations in the whole brain.…”

Machine Learning in Mental Health

“…The service consists of more than one million communities, called 'subreddits', and has more than 330 million monthly active users. 3 Live Journal ( w w w.livejournal.com ) is a social networking service with approximately 30 million monthly visitors. Users have a profile page, can maintain a personal blog, connect and communicate with others, and form an online community in the form of a collective blog [ 133 ].…”

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

“…Multimodal medical image registration algorithms can be mainly divided into three categories: pixel-level registration [1][2][3][4][5][6][7], feature-level registration [8][9][10][11][12][13][14] and deep learning based registration [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. One type of image registration method is the pixel-level registration which mainly investigates the pixel relationship between two images.…”

“…The deep learning based registration method did not develop until recent years, especially for multimodal images, and it still faces many challenges until now. In contrast to other fields in medical image process and analysis such as image segmentation [23][24][25] and denoising [26][27][28], deep learning based methods have yet not settled on the best way to apply this technique into medical image registration. The main challenges are as follows:  Poor applicability.…”