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Eq. ( 6.7 )asfollows:
X T Y ʲ
1 n
( ʱ , ʲ ) =
1
ʱ t ,
T YXX T Y ʱ +
b t :
argmin
ʱ
P 1
2 ʱ
ʱ
(6.8)
s.t.
y T
C
ʻ
ʻ
ʱ =
0
,
0 n ʱ
1 n ,
where the bias b t is derived analogously to the conventional L2-SVM approach. As
a matter of fact, Eq. ( 6.8 ) is a simple reformulation of the conventional dual formu-
lation of L2-SVM, which can be solved by exploiting any of the several approaches
proposed in the last decades (Shawe-Taylor and Sun 2011 ). If, instead, we fix ʱ
to some constant value
ʱ , which also satisfies the constrains, we can reformulate
Eq. ( 6.7 ) in the following manner:
T YX
ʲ t
1
T I ʲ +
:
argmin
ʲ
P 2
( ʲ , ʱ ) =
2 ʲ
ʱ
ʲ
(6.9)
( 1 ʻ)
1 d ʲ ( 1 ʻ)
s.t.
1 d .
ʻ
ʻ
Equation ( 6.9 ) has a closed form solution, as we have to identify the minimum of
a paraboloid in a box, which is characterized by an identity Hessian matrix:
max
min (
T YX T
(
1
ʻ)
1
ʻ)
ʲ t
=
1 d ,
1 d ,
ʱ
.
(6.10)
ʻ
ʻ
Consequently, we propose an approach to solve Eq. ( 6.7 ) which is detailed in
Algorithm 1. For this, solutions of Eqs. ( 6.8 ) and ( 6.9 ) are iteratively found. The
former case can therefore be solved with any of the methods surveyed by (Shawe-
Taylor and Sun 2011 ).
Particularly, we have also described Algorithm 2 which is focused on the adoption
of the SMO algorithm (Platt 1998 ;Keerthietal. 2001 ) for solving Eq. ( 6.8 ). Note
that, since, at every optimization step, two
ʱ i coefficients are optimized by the SMO,
in order to better balance the overall L1-L2 optimization procedure we can run 2
iterations of SMO and, then, update
ʲ i ∈{ 1 ,..., d } .
Algorithm 1 : Algorithm for solving Problem (6.7)
Data : D n ,
ʻ
, C ,
ʵ
and numerical precision
w ,
b
Result :
t
=
0, ʱ t
=
0 n , ʲ t
=
0 d ;
repeat
ʱ t + 1
,
b t + 1
=
argmin ʱ
P 1
( ʱ , ʲ t )
;
max
min ( 1 ʻ)
1 YX T ;
, ʱ
( 1 ʻ)
t
ʲ t + 1 =
1 d
,
1 d
ʻ
ʻ
+
t=t+1;
until
;
ʲ t ʲ t 1
2
2
2
ʱ t
ʱ t 1
+
<
ʻ
ʻ
X T Y ʱ t
+ ʲ t ,b
w =
return
=
b t
 
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