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Transcript of Rutgers, The State University of New Jersey Iterative Embedding with Robust Correction using...
Rutgers, The State University of New Jersey
Iterative Embedding with Robust Correction using Feedback of Error Observed
Praneeth Vepakomma1
Ahmed Elgammal2
Department of Statistics, Rutgers1,
Department of Computer Science, Rutgers2
Electrical & Computer Engineering, FIU1
Smart Public Safety Solutions, Motorola Solutions1
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Introduction
• Iterative Manifold Learning• Interleaving Iterative Embedding
& Feedback from Error• Damping Effect of Outliers• M-Estimation
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Interleaved Approach:
Manifold Learning
M-Estimation
At Iteration t:
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Feedback Loop With Adjustment of Weights:
Big Picture
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Majorization Minimization:
• Uses a surrogate objective• The surrogate bounds the original objective• Exception: Touches the original objective at only one point• Incremental Optimization (Monotonic Convergence)
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Proposed Majorization Function:
Linear Constraint:
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MM Routine:
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Robust Multiplicative Updates:
M-Estimation with Geman Mcclure Function:
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We Show Existence of Sharpest Majorizer:
Implicit Positivity Constraints
Majorization Function
Condition:
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