Feature grouping and selection: A graph-based approach

A Graph-Based Approach to Feature Selection

Simultaneous grouping pursuit and feature selection over an undirected graph.

In high-dimensional regression, grouping pursuit and feature selection have their own merits while complementing each other in battling the curse of dimensionality. To seek a parsimonious model, we perform simultaneous grouping pursuit and feature selection over an arbitrary undirected graph with each node corresponding to one predictor. When the corresponding nodes are reachable from each othe...

A Hypergraph-Based Approach to Feature Selection

In many data analysis tasks, one is often confronted with the problem of selecting features from very high dimensional data. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. To overcome this problem it is frequently assumed that either features independently influence the class variable or do so only involving pairwise feature...

A Graph-based Semi-Supervised Learning Approach and its Feature Selection

With the lack of labeled data, the learning accuracy of a supervised learning algorithm deteriorates. Meanwhile, it is more easy to collect plenty of unlabeled data. Furthermore, a graph can be used to express the underlying distribution of data in the dataset. Thus, a classification problem is converted to a graph partition problem. One typical graph-based semi-supervised learning algorithm is...

Common-fate grouping as feature selection.

The visual system groups elements within the visual field that are physically separated yet similar to each other. Although grouping processes have been intensely studied for a century, the mechanisms of grouping remain elusive. We propose that a primary mechanism for grouping by common fate is attentional selection of a direction of motion. A unique prediction follows from this account: that t...