Interactive Learning using Manifold Geometry Eric Eaton, Gary Holness, and Daniel McFarlane Lockheed Martin Advanced Technology Laboratories Artificial Intelligence Research Group This work was supported by internal funding from Lockheed Martin and the National Science Foundation under NSF ITR #0325329.
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Interactive Learning using Manifold Geometry Eric Eaton, Gary Holness, and Daniel McFarlane Lockheed Martin Advanced Technology Laboratories Artificial.
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Interactive Learning using Manifold Geometry
Eric Eaton, Gary Holness, and Daniel McFarlane
Lockheed Martin Advanced Technology LaboratoriesArtificial Intelligence Research Group
This work was supported by internal funding from Lockheed Martin and the National Science Foundation under NSF ITR #0325329.
2Eric Eaton, Gary Holness, & Daniel McFarlane - Interactive Learning using Manifold Geometry
Introduction: Motivation
Information monitoring systems use a scoring function ff to focus user attention
– ff is customized to the current situation
– Often, no data are available to learn ff
Maritime Situational Awareness
Network Security Monitoring
– Users require fine control over the scoring function
We propose an interactive interactive learninglearning method that enables the user to iteratively refine ff
3Eric Eaton, Gary Holness, & Daniel McFarlane - Interactive Learning using Manifold Geometry
Introduction: Interactive Refinement
Uses a combination of manual input and machine learning:
1. The user manually selects and repositions a data point
2. The system relearns the model ff, and updates the scatterplot
Key idea: each adjustment should generalize naturally to the model
We use least squares with Laplacian regularization to learn ff, based on the manifold underlying the data
1D Projection of Data
Rel
evan
cy
User View Model View
4Eric Eaton, Gary Holness, & Daniel McFarlane - Interactive Learning using Manifold Geometry
Related Work: Interactive Learning
Crayons tool for interactive object classification (Fails & Olsen, 2003)
Interactive decision tree construction (Ware et al., 2001)
Interactive visual clustering (desJardins et al., 2008)
Feature selection(Dy & Brodley, 2000)
Hierarchical clustering (Wills, 1998)
Crayons by Fails & Olsen(Figure used with permission)
Interactive Visual Clustering by desJardins et al.(Figure used with permission)
Initial viewAfter 2
adjustmentsAfter 14
adjustments
5Eric Eaton, Gary Holness, & Daniel McFarlane - Interactive Learning using Manifold Geometry
Related Work: Interactive vs Active Learning
Active learning – selects instances for labeling by an oracle (Cohn et al., 1996; McCallum & Nigam, 1998; Tong, 2001)
Interactive ML Active Learning
Starts with… Unlabeled data IncorrectIncorrect model
Unlabeled data NoNo model
Selection of instances
UserUser determines adjustments
SystemSystem selects instances for labeling
GoalCollaborate with Collaborate with the userthe user to define or adjust a model
Minimize number of Minimize number of labelslabels needed to learn a model
6Eric Eaton, Gary Holness, & Daniel McFarlane - Interactive Learning using Manifold Geometry
Data setwhere
The user supplies the initialscoring function
– We used a linear function for
Current scoring function is givenby f (initially )
The user adjusts the score of individual data points to change f until it matches the true (hidden) function F
– Details of each instance are available in a side panel
– User selects and drags an instance up or down to change its score
This work was supported by internal funding from Lockheed Martin and the National Science Foundation under NSF ITR #0325329.
20Eric Eaton, Gary Holness, & Daniel McFarlane - Interactive Learning using Manifold Geometry
References
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