Feature selection

Time series feature learning with labeled and unlabeled data

Time series classification has attracted much attention in the last two decades. However, in many real-world applications, the acquisition of sufficient amounts of labeled training data is costly, while unlabeled data is usually easily to be …

Query-oriented citation recommendation based on network correlation

With the rapid proliferation of information technology, researchers find it more and more difficult to rapidly find appropriate reference papers for an authoring paper. Citation recommendation aims to overcome this problem by providing a list of …

Task sensitive feature exploration and learning for multitask graph classification

Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To date, all existing MTL methods have been designed for tasks with feature-vector represented instances, but cannot be applied to structure data, such as …

Multi-graph-view subgraph mining for graph classification

In this paper, we formulate a new multi-graph-view learning task, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, …

Finding the best not the most: regularized loss minimization subgraph selection for graph classification

Classification on structure data, such as graphs, has drawn wide interest in recent years. Due to the lack of explicit features to represent graphs for training classification models, extensive studies have been focused on extracting the most …

Multi-graph-view Learning for Graph Classification

Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new …