Transcript Slides1

Optimization Problem Based on L2,1-norms
Xiaohong Chen
19-10-2012
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Outline
Efficient and robust feature selection via
joint l2,1-norm minimzation
Robust and discriminative distance for
multi-instance learning
 Its application…
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Outline
Efficient and robust feature selection via
joint l2,1-norm minimization
Robust and discriminative distance for
multi-instance learning
 Its application…
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Efficient and robust feature selection
via joint l2,1-norm minimzation
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Robust Feature Selection Based on l21-norm
Given training data {x1, x2,…, xn} and the associated class
labels {y1,y2,…, yn}
Least square regression solves the following optimizaiton
problem to obtain the projection matrix W
Add a regularization R(W) to the robust version of LS,
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Robust Feature Selection Based on l21-norm
Possible regularizations
Ridge regularization
Lasso regularization
Lasso regularization
Penalize all c regression coefficients
corresponding to a single feature as a whole
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Robust Feature Selection Based on l21-norm
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Robust Feature Selection Based on l21-norm
Denote
(14)
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Robust Feature Selection Based on l21-norm
Then we have
(19)
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The iterative algorithm to solve problem (14)
Theorem1: The algorithm will monotonically decrease the objective
of the problem in Eq.(14) in each iteration, and converge to the global
optimum of the problem.
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Proof of theorem1
2ab  a  b
2
2
a 2 b2
a

2b 2b
u
a2
b2
a
b
2b
2b
u
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Proof of theorem1
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(1)
(2)
(1)+(2)
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Outline
Efficient and robust feature selection via
joint l2,1-norm minimization
Robust and discriminative distance for
multi-instance learning
 Its application…
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Robust and discriminative distance for
multi-instance learning
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Multi-instance learning
多示例学习中，训练集由若干个具有概念标记的包(bag)组成，
每个包包含若干个没有概念标记的示例。若一个包中至少有
一个正例，则该包被标记为正(positive)，若一个包中所以示
例都是反例，则该包被标记为反(negative),通过对训练包的学
习，希望学习系统尽可能正确地对训练集之外的包的概念标
记进行预测。
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The illustration of MIL
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Notations
Given N training bags
classes.
and K conceptual
Each bag contains a number of instances
Given the class memberships of the input data, denoted as
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Notations
First, we represent every class as a super-bag that comprises the
instances of all its training
, where
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Objective to learn class specific distance metrics
For a given class, Ck,, we solve the following optimization problem:
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Algorithm and its analysis
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Algorithm and its analysis
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Algorithm and its analysis
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Algorithm and its analysis
On the other hand,
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Algorithm and its analysis
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Algorithm and its analysis
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Algorithm and its analysis
Therefore, the objective value of the problem of (6) is decreased in
each iteration till convergences.
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Outline
Efficient and robust feature selection via
joint l2,1-norm minimzation
Robust and discriminative distance for
multi-instance learning
 Its application…
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Its application
For example:

min
W T ( xi  x j )( xi  x j )TW
xi , x j同类

min
W T ( xi  x j )( xi  x j )TW
W
xi , x j 不同类
min
W
AW
BW
2,1
AW
BW
2,1
2,1
2,1
  CW
2,1
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Reference
[1]F.Nie, D.Xu, X.Cai, and C.Ding. Efficient and robust feature selection via
joint l2,1-norm minimzation. NIPS 2010.
[2] H.Wang, F.Nie and H.Huang. Robust and discriminative distance for multiinstance learning, CVPR 2012: 2919-2924
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Thanks! Q&A
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