Ming Ji

JMLR Journal 2013 Journal Article

Parallel Vector Field Embedding

• Binbin Lin
• Xiaofei He
• Chiyuan Zhang
• Ming Ji

We propose a novel local isometry based dimensionality reduction method from the perspective of vector fields, which is called parallel vector field embedding (PFE). We first give a discussion on local isometry and global isometry to show the intrinsic connection between parallel vector fields and isometry. The problem of finding an isometry turns out to be equivalent to finding orthonormal parallel vector fields on the data manifold. Therefore, we first find orthonormal parallel vector fields by solving a variational problem on the manifold. Then each embedding function can be obtained by requiring its gradient field to be as close to the corresponding parallel vector field as possible. Theoretical results show that our method can precisely recover the manifold if it is isometric to a connected open subset of Euclidean space. Both synthetic and real data examples demonstrate the effectiveness of our method even if there is heavy noise and high curvature. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

ICML Conference 2012 Conference Paper

A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound

• Ming Ji
• Tianbao Yang
• Binbin Lin
• Rong Jin 0001
• Jiawei Han 0001

TIST Journal 2011 Journal Article

MoveMine

• Zhenhui Li
• Jiawei Han
• Ming Ji
• Lu-An Tang
• Yintao Yu
• Bolin Ding
• Jae-Gil Lee
• Roland Kays

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining. For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

IJCAI Conference 2009 Conference Paper

• Xiaofei He
• Ming Ji
• Hujun Bao

Recently graph based dimensionality reduction has received a lot of interests in many fields of information processing. Central to it is a graph structure which models the geometrical and discriminant structure of the data manifold. When label information is available, it is usually incorporated into the graph structure by modifying the weights between data points. In this paper, we propose a novel dimensionality reduction algorithm, called Constrained Graph Embedding, which considers the label information as additional constraints. Specifically, we constrain the space of the solutions that we explore only to contain embedding results that are consistent with the labels. Experimental results on two real life data sets illustrate the effectiveness of our proposed method.