Dimensionality reduction (DR) is an efficient approach to reduce the size of
data by capturing the informative intrinsic features and discarding the noise. DR
methods can be grouped through a variety of categories, e.g. supervised/ unsupervised,
linear/non-linear or parametric/non-parametric. Objective function based
methods can be grouped into convex and non...
In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...
In histopathological image analysis, image classification as well as pattern detection play a crucial role in the diagnosis and treatment process since the goal is to not only differentiate cancer types but also identify cancerous manifestations. Fully supervised learning strategies tend to address these problems using manually annotated cancerous regions...