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Graph Regularized Transductive Classification on Heterogeneous Information Networks
- Ming Ji, Yizhou Sun, Marina Danilevsky, Jiawei Han, Jing Gao
- Computer ScienceECML/PKDD
- 20 September 2010
This paper considers the transductive classification problem on heterogeneous networked data which share a common topic and proposes a novel graph-based regularization framework, GNetMine, to model the link structure in information networks with arbitrary network schema and arbitrary number of object/link types.
A Variance Minimization Criterion to Active Learning on Graphs
- Ming Ji, Jiawei Han
- Computer Science, MathematicsInternational Conference on Artificial…
- 21 March 2012
This study considers the problem of active learning over the vertices in a graph, without feature representation, based on the common graph smoothness assumption, which is formulated in a Gaussian random field model and produces a theoretically more robust classifier.
MoveMine: mining moving object databases
The system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining and a user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods.
MoveMine: Mining moving object data for discovery of animal movement patterns
A moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis is introduced, which will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies.
Ranking-based classification of heterogeneous information networks
- Ming Ji, Jiawei Han, Marina Danilevsky
- Computer ScienceKnowledge Discovery and Data Mining
- 21 August 2011
A novel ranking-based iterative classification framework that generates more accurate classes than the state-of-art classification methods on networked data, but also provides meaningful ranking of objects within each class, serving as a more informative view of the data than traditional classification.
A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization
- Xiaofei He, Ming Ji, Chiyuan Zhang, H. Bao
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 October 2011
This paper considers the feature selection problem in unsupervised learning scenarios, which is particularly difficult due to the absence of class labels that would guide the search for relevant information, and proposes two novel feature selection algorithms which aim to minimize the expected prediction error of the regularized regression model.
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
A simple algorithm is developed to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression to achieve an improved regression error bound better than existing bounds of supervised learning.
Graph Embedding with Constraints
This paper proposes a novel dimensionality reduction algorithm, called Constrained Graph Embedding, which considers the label information as additional constraints, and constrain the space of the solutions that are explored only to contain embedding results that are consistent with the labels.
Learning search tasks in queries and web pages via graph regularization
A novel graph-based regularization algorithm is designed for search task prediction by leveraging the graph to simultaneously classify queries and web pages into the popular search tasks by exploiting their content together with click-through logs.
Parallel vector field embedding
A novel local isometry based dimensionality reduction method from the perspective of vector fields, which is called parallel vector field embedding (PFE), which can precisely recover the manifold if it is isometric to a connected open subset of Euclidean space.