MLkNN

MLkNN stands for Marginalized Locally Semi-Supervised Label Propagation. It is a machine learning algorithm used for classification tasks, particularly in scenarios where labeled data is scarce and unlabeled data is abundant. The core idea behind MLkNN is to leverage the information present in unlabeled data to improve the classification accuracy of models trained on limited labeled data.

This algorithm falls under the umbrella of semi-supervised learning. Semi-supervised learning aims to build classifiers by using a small amount of labeled data along with a large amount of unlabeled data. MLkNN specifically incorporates a label propagation mechanism. In label propagation, labels are gradually passed from labeled instances to their unlabeled neighbors in a data graph. The "marginalized" aspect refers to how it handles the propagation process, considering the marginal probabilities of labels. The "locally semi-supervised" part highlights that the learning process is influenced by the local structure of the data, where instances that are close to each other are assumed to have similar labels.

MLkNN works by constructing a graph where data points are nodes and edges represent the similarity between them. It then iteratively propagates label information through this graph. The algorithm estimates the label distribution for each unlabeled instance based on the labels of its neighbors and the underlying data structure. This iterative process continues until the label assignments stabilize. MLkNN is known for its effectiveness in domains like image and text classification where obtaining large amounts of labeled data can be expensive and time-consuming.