Towards Active Learning on Graphs: An Error Bound Minimization Approach

Gu, Quanquan

doi:10.1109/icdm.2012.72

Cited by 50 publications

(68 citation statements)

References 8 publications

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“…Active learning on graphs is an important technique that applies to many problems, such as Random Walk. Gu and Han [2012] proposed a nonadaptive active learning approach on graphs, based on generalization error bound minimization. When considering the traveling person's trajectories and decision making, we cannot ignore that there are person groups moving together.…”

Section: Related Workmentioning

confidence: 99%

Rationality Analytics from Trajectories

Liu

Wang

2015

ACM Trans. Knowl. Discov. Data

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The availability of trajectories tracking the geographical locations of people as a function of time offers an opportunity to study human behaviors. In this article, we study rationality from the perspective of user decision on visiting a point of interest (POI) which is represented as a trajectory. However, the analysis of rationality is challenged by a number of issues, for example, how to model a trajectory in terms of complex user decision processes? and how to detect hidden factors that have significant impact on the rational decision making? In this study, we propose Rationality Analysis Model (RAM) to analyze rationality from trajectories in terms of a set of impact factors. In order to automatically identify hidden factors, we propose a method, Collective Hidden Factor Retrieval (CHFR), which can also be generalized to parse multiple trajectories at the same time or parse individual trajectories of different time periods. Extensive experimental study is conducted on three large-scale real-life datasets (i.e., taxi trajectories, user shopping trajectories, and visiting trajectories in a theme park). The results show that the proposed methods are efficient, effective, and scalable. We also deploy a system in a large theme park to conduct a field study. Interesting findings and user feedback of the field study are provided to support other applications in user behavior mining and analysis, such as business intelligence and user management for marketing purposes.

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Section: Related Workmentioning

confidence: 99%

Rationality Analytics from Trajectories

Liu

Wang

2015

ACM Trans. Knowl. Discov. Data

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“…Other algorithms exist that use a measure of confidence for the labels currently given, such as entropy [13] or variance [14]. Finally, the expected error reduction [15–18] algorithms come from the fact that the measure of performance is mostly the risk and that it makes more sense to minimize it directly rather than some other indirect criteria. Our work belongs to this last category.…”

Section: Related Workmentioning

confidence: 99%

“…In the data-independent case, the optimal allocation does not depend on the samples received so far. It can be related to one-shot active learning, as defined in [18], in which the allocation of the budget is decided before sampling any instances. In the data-dependent case, the label given by the classifier at time t is also considered.…”

Section: Independent Clustersmentioning

confidence: 99%

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Optimism in Active Learning

Collet

Pietquin

2015

Computational Intelligence and Neuroscience

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Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the effect of the addition of a pair is not known in advance. It can still be estimated with the pairs already in the training set. The online minimization of the classification error involves a tradeoff between exploration and exploitation. This is a common problem in machine learning for which multiarmed bandit, using the approach of Optimism int the Face of Uncertainty, has proven very efficient these last years. This paper introduces three algorithms for the active learning problem in classification using Optimism in the Face of Uncertainty. Experiments lead on built-in problems and real world datasets demonstrate that they compare positively to state-of-the-art methods.

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“…Notice that the key quantity is the posterior marginal distribution P Yq=y,y (Y i = y ) in computing (6) and P y (Y q = y) in (7). An efficient computation of the posterior marginal would lead to an algorithm for PREDICT due to (3), and also to an algorithm for QUERY due to (5).…”

Section: Expected Error Minimization (Eem)mentioning

confidence: 99%

Graph-based active learning: A new look at expected error minimization

Jun

Nowak

2016

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance. The exact computation of EEM optimally balances exploration and exploitation. In practice, however, EEM-based algorithms employ various approximations due to the computational hardness of exact EEM. This can result in a lack of either exploration or exploitation, which can negatively impact the effectiveness of active learning. We propose a new algorithm TSA (Two-Step Approximation) that balances between exploration and exploitation efficiently while enjoying the same computational complexity as existing approximations. Finally, we empirically show the value of balancing between exploration and exploitation in both toy and real-world datasets where our method outperforms several state-of-the-art methods.

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Towards Active Learning on Graphs: An Error Bound Minimization Approach

Cited by 50 publications

References 8 publications

Rationality Analytics from Trajectories

Rationality Analytics from Trajectories

Optimism in Active Learning

Graph-based active learning: A new look at expected error minimization

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