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Direct discriminative bag mapping for multi-instance learning
Multi-instance learning (MIL) is useful for tackling labeling ambiguity in learning tasks, by allowing a bag of instances to share one …
Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their …
Tri-party deep network representation
Information network mining often requires examination of linkage relationships between nodes for analysis. Recently, network …
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Mining top-k minimal redundancy frequent patterns over uncertain databases
Frequent pattern mining from uncertain data has been paid closed attention due to most of the real life databases contain data with …
DOI
Multi-graph-view learning for complicated object classification
In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a …
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 …
DOI
Attribute weighting: how and when does it work for Bayesian Network Classification
A Bayesian Network (BN) is a graphical model which can be used to represent conditional dependency between random variables, such as …
DOI
Dual instance and attribute weighting for Naive Bayes classification
Naive Bayes (NB) network is a popular classification technique for data mining and machine learning. Many methods exist to improve the …
DOI
Exploring features for complicated objects: cross-view feature selection for multi-instance learning
In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming …
DOI
Multi-graph learning with positive and unlabeled bags
In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for …
DOI