1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao...

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1 Multi-Task Semi- Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research

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What Analyst Processes Individual Signatures Processed by Supervised Classifiers Message: Analyst Places Classification of Any Given Item Within Context of All Items in the Scene Supervised Classifier Classifies Each Item in Isolation

Transcript of 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao...

Page 1: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.

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Multi-Task Semi-Supervised Underwater Mine Detection

Lawrence Carin, Qiuhua Liu and Xuejun LiaoDuke University

Jason StackOffice of Naval Research

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Intra-Scene Context

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What Analyst Processes Individual Signatures Processedby Supervised Classifiers

Message:

Analyst Places Classification of Any Given Item Within Context of All Items in the SceneSupervised Classifier Classifies Each Item in Isolation

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Decision surface based on labeled data (supervised)

Decision surface based on labeled & Unlabeled data (semi-supervised)

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Inter-Scene Context

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Message

Humans are very good at exploiting context, both within a given scene and across multiple scenes

Intra-scene context: semi-supervised learning

Inter-scene context: multi-task and transfer learning

A major focus of machine learning these days

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Data Manifold Representation Based on Markov Random Walks

Given X={x1, …,xN}, first construct a graph G=(X,W), with the affinity matrix W, where the (i, j)-th element of W is defined by a Gaussian kernel:

we consider a Markov transition matrix A, which defines a Markov random walk, where the (i, j)-th element:

gives the probability of walking from xi to xj by a single step.

The one-step Markov random work provides a local similarity measure between data points.

)2/exp( 22

ijiij xxw

N

k ik

ijij

w

wa

1

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Semi-Supervised Multitask Learning(1/2)

Semi-supervised MTL: Given M partially labeled data manifolds, each defining a classification task, we propose a unified sharing structure to learn the M classifiers simultaneously.

The Sharing Prior: We consider M PNBC classifiers, parameterized by

The M classifiers are not independent

but coupled by a joint prior distribution:

,m....,2,1 Mm

M

mmmM pp

1111 ),..,|(),..,(

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Semi-Supervised Multitask Learning(2/2)

With

The normal distributions indicates the meta-knowledge indicating how the present task should be learned, based on the experience with a previous task.

When there are no previous tasks, only the baseline prior is used by setting m=1 =>PNBC.

Sharing tasks to have similar , not exactly the same(advantages over the Dirac delta function used in previous MTL work).

s'

1

1

211 ),;()|(

11),..,|(

m

lmllmmmm Np

mp Iγ

Baseline prior Prior transferred from previous tasks

Balance parameter

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Thanks