[IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander,...

6
,((( ,17(51$7,21$/ :25.6+23 21 0$&+,1( /($51,1* )25 6,*1$/ 352&(66,1* 6(37 ± 6$17$1'(5 63$,1 $ 129(/ 6&+(0( )25 ',))86,21 1(7:25.6 :,7+ /($67648$5(6 $'$37,9( &20%,1(56 -HV XV )HUQ DQGH]%HV /XLV $ $]SLFXHWD5XL] 0DJQR 7 0 6LOYD DQG -HU RQLPR $UHQDV*DUF ÕD 8QLY &DUORV ,,, GH 0DGULG 6SDLQ 8QLY 3ROLW HFQLFD GH 0DGULG 6SDLQ 8QLY RI 6× DR 3DXOR %UD]LO {MHVXVIEHV MDUHQDV}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² $GDSWLYH ¿OWHULQJ OHDVW VTXDUHV DGDSWLYH QHW ZRUNV DI¿QH FRPELQDWLRQ GLIIXVLRQ ,1752'8&7,21 $GDSWLYH QHWZRUNV KDYH DWWUDFWHG FRQVLGHUDEOH DWWHQWLRQ ODWHO\ DV DQ HI¿FLHQW VROXWLRQ WR HVWLPDWH FHUWDLQ SDUDPHWHUV RI LQWHUHVW XV LQJ WKH LQIRUPDWLRQ IURP GDWD FROOHFWHG DW QRGHV GLVWULEXWHG RYHU D UHJLRQ >±@ ,Q PDQ\ DSSOLFDWLRQV HJ VRXUFH ORFDOL]DWLRQ DQG HQ YLURQPHQW PRQLWRULQJ WKHVH QHWZRUNV PXVW WUDFN WKH YDULDWLRQV LQ WKH GDWD VWDWLVWLFV ZKLFK MXVWL¿HV WKH QHHG IRU DGDSWLYHQHVV >@ $G GLWLRQDOO\ WKH QHWZRUN DOJRULWKPV PXVW H[KLELW ORZ FRPSXWDWLRQDO FRVW DQG IDVW FRQYHUJHQFH WR VDWLVI\ VRPH HQHUJ\ DQG SHUIRUPDQFH UH TXLUHPHQWV ZKLFK LV D TXLWH FKDOOHQJLQJ VLJQDO SURFHVVLQJ SUREOHP ,Q WKLV SDSHU ZH IRFXV RQ GLVWULEXWHG VROXWLRQV RYHU GLIIXVLRQ QHWZRUNV LQ ZKLFK HYHU\ QRGH H[FKDQJHV LQIRUPDWLRQ ZLWK LWV QHLJK ERULQJ QRGHV DW HDFK WLPH LQVWDQW n )LJ VKRZV D QHWZRUN FRP SRVHG E\ N QRGHV GLVWULEXWHG RYHU VRPH UHJLRQ 7KH VHW RI QRGHV FRQQHFWHG WR QRGH k LQFOXGLQJ k LWVHOI LV FDOOHG WKH QHLJKERUKRRG RI QRGH k DQG GHQRWHG E\ N k ZLWK FDUGLQDOLW\ n k $W LQVWDQW n HYHU\ QRGH k WDNHV D PHDVXUHPHQW {d k (n), u k (n)} WR HVWLPDWH D FRPPRQ FROXPQ SDUDPHWHU YHFWRU wo(n) ZKHUH d k (n) UHSUHVHQWV D GHVLUHG VLJQDO DQG u k (n) GHQRWHV D OHQJWKM LQSXW UHJUHVVRU FROXPQ YHFWRU 7KH GHVLUHG VLJQDO d k (n) LV UHODWHG WR u k (n) YLD WKH XVXDO OLQHDU UH JUHVVVLRQ PRGHO >@ LH d k (n)= u T k (n)wo(n 1)+ v k (n) ZKHUH v k (n) SOD\V WKH UROH RI D ]HURPHDQ QRLVH GLVWXUEDQFH XQFRUUHODWHG ZLWK u (n) =1, 2, ···, k, ···,N DQG ZLWK YDULDQFH σ 2 v k 7KH GLIIXVLRQ VWUDWHJ\ LV FRPPRQO\ SHUIRUPHG LQ WZR VWDJHV DGDSWDWLRQ DQG FRPELQDWLRQ 7KH RUGHU LQ ZKLFK WKHVH VWDJHV DUH SHUIRUPHG OHDGV WR WZR SRVVLELOLWLHV DGDSWWKHQFRPELQH &7$ DQG FRPELQHWKHQDGDSW $7& >@ ,Q WKLV SDSHU ZH RQO\ FRQVLGHU WKH 7KH ZRUN RI )HUQ DQGH]%HV $]SLFXHWD5XL] DQG $UHQDV*DUF ÕD ZDV SDUWO\ VXSSRUWHG E\ 0,&,11 SURMHFWV 7(& DQG 35,3,%,1 7KH ZRUN RI 6LOYD ZDV SDUWO\ VXSSRUWHG E\ &13T XQGHU *UDQW DQG )$3(63 XQGHU *UDQW N k k N 1 2 3 {d k (n), u k (n)} {d1(n), u1(n)} {d2(n), u2(n)} {d (n), u (n)} {dN (n), uN (n)} {d3(n), u3(n)} )LJ 'LIIXVLRQ QHWZRUN ZLWK N QRGHV DW WLPH n HYHU\ QRGH k WDNHV D PHDVXUHPHQW {d k (n), u k (n)} 7KH QHLJKERUKRRG RI QRGH k LQ WKLV QHWZRUN LV N k = {1, 2, , k} DQG n k =4 $7& VWUDWHJ\ VLQFH WKH UHVXOWV FDQ EH H[WHQGHG VWUDLJKWIRUZDUGO\ WR &7$ ,Q $7& HDFK QRGH k XSGDWHV LWV ORFDO HVWLPDWH ψ k (n) XVLQJ WKH FRPELQHG HVWLPDWH IURP WKH SUHYLRXV LWHUDWLRQ w k (n 1) 7KHQ WKH ORFDO HVWLPDWHV RI WKH QRGHV EHORQJLQJ WR WKH QHLJKERUKRRG N k DUH FRPELQHG WR REWDLQ w k (n) LH ψ k (n)= w k (n1)+˜ μ k (n)u k (n) d k (n) u T k (n)w k (n1) w k (n)= ∈N k c k (n)ψ (n), ZKHUH ZH KDYH DVVXPHG WKDW WKH QRUPDOL]HG OHDVWPHDQVTXDUHV 1/06 DOJRULWKP LV XVHG LQ WKH DGDSWDWLRQ VWDJH VLQFH ˜ μ k (n) μ k δ + u k (n) 2 , ZKHUH μ k LV D VWHS VL]H δ LV D UHJXODUL]DWLRQ IDFWRU VPDOO SRVLWLYH FRQVWDQW DQG · GHQRWHV WKH (XFOLGHDQ QRUP 7KH ORFDO ψ k DQG FRPELQHG w k HVWLPDWHV DUH FROXPQ YHFWRUV RI OHQJWK M DQG WKH FRPELQDWLRQ ZHLJKWV c k (n) ∈N k DUH DVVLJQHG WR HDFK QRGH FRQQHFWHG WR QRGH k LQFOXGLQJ k LWVHOI 0RVW SDSHUV LQ WKH OLWHUDWXUH DVVXPH ¿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©2012 IEEE

Transcript of [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander,...

Page 1: [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain (2012.09.23-2012.09.26)] 2012 IEEE International Workshop on Machine Learning

† ‡ ∗ †

† ‡ ∗

{ }

nN

k kk Nk nk nk {dk(n),uk(n)}

wo(n) dk(n)uk(n) M

dk(n) uk(n)dk(n) = uT

k (n)wo(n−1)+vk(n)vk(n)

u�(n) � = 1, 2, · · ·, k, · · ·, N σ2vk

Nk k

N1

2

3

{dk(n),uk(n)}

{d1(n),u1(n)}

{d2(n),u2(n)}

{d�(n),u�(n)}

{dN (n),uN (n)}

{d3(n),u3(n)}

N n k{dk(n),uk(n)} k

Nk = {1, 2, �, k} nk = 4

k ψk(n)wk(n−1)

Nk

wk(n)

ψk(n)=wk(n−1)+μk(n)uk(n)[dk(n)−u

T

k (n)wk(n−1)]

wk(n)=∑

�∈Nk

c�k(n)ψ�(n),

μk(n) �μk

δ + ‖uk(n)‖2,

μk δ‖ · ‖ ψk

wk Mc�k(n) � ∈ Nk

k k

978-1-4673-1026-0/12/$31.00 ©2012 IEEE

Page 2: [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain (2012.09.23-2012.09.26)] 2012 IEEE International Workshop on Machine Learning

k ψk(n)ψk(n− 1)

• ψk(n)

ψk(n) kwk(n)

Nk k kbk nk = nk−1

Nk b(m)k , m = 1, . . . , nk

mth bk

mth k kNk = {1, 2, �} bk = [ 1 2 � ]T b

(3)k = �

ck(n) ck(n)nk nk

k ckk(n)k

ck(n) = [c1k(n) c2k(n) c�k(n) ckk(n)]T

ck(n)=[c1k(n) c2k(n) c�k(n)]T

ck(n) 1T ck(n) = 1T ck(n) +ckk(n) = 1 1

wk(n) =wk(n) −wo(n)

k(n) = ‖wk(n)‖2

(n) =1

N

N∑k=1

k(n),

n →∞

ck(n) = arg min E‖wk(n)−wo‖2

1Tck(n) = 1

k = 1, 2, . . . N E

wo

wo

• {ψk(n), n ≥ 0}Ψk EΨk = wo

k ∈ {1, 2, . . . , N}

• �, m ∈ Nk E [ψ�(n)−wo]T [ψm(n)−wo]

≈ [ψ�(n)− ψ�(n− 1)]T [ψm(n)− ψm(n− 1)] .

k wo

ψk(n) = ψk(n− 1) + μk(n)uk(n)ek(n),

Page 3: [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain (2012.09.23-2012.09.26)] 2012 IEEE International Workshop on Machine Learning

k = 1, . . . , N μk(n)ek(n)

ek(n) = dk(n)− uT

k (n)ψk(n− 1) � dk(n)− yk(n).

ψk(n)n− 1

wk(n) = ckk(n)ψk(n) +

nk∑m=1

c(m)k (n)w

b(m)k

(n−1).

wk(n)

ck(n) k = 1, 2, . . . , Nck(n)

ckk(n) = 1−

nk∑m=1

c(m)k (n).

ck(n)

Jk(n) =n∑

i=1

β(n, i)e2k(n, i),

β(n, i)

ek(n, i) = dk(i)− yk(n, i),

yk(n, i) =

nk∑m=1

c(m)k (n)y

k,b(m)k

(i)+

[1−

nk∑m=1

c(m)k (n)

]yk(i)

= yk(i) +

nk∑m=1

c(m)k (n)

[y

k,b(m)k

(i)− yk(i)]

k iNk

nyk,p(n) = u

T

k (n)wp(n− 1), p ∈ Nk.

ek(n, i) = ek(i) +

nk∑m=1

c(m)k (n)

[yk(i)− y

k,b(m)k

(i)].

c(�)k (n) � = 1, 2, . . . , nk

∂Jk(n)

∂c(�)k (n)

= 2

n∑i=1

β(n, i)ek(n, i)[yk(i)− y

k,b(�)k

(i)].

n∑i=1

nk∑m=1

β(n, i)c(m)k (n)y

k,b(m)k

(i)yk,b

(�)k

(i)

=n∑

i=1

β(n, i)ek(i)yk,b

(�)k

(i),

yk,p(n) � yk,p(n)− yk(n) p ∈ Nk

k nk

Pk(n)ck(n) = zk(n),

Pk(n) nk

[Pk(n)]m,�

=n∑

i=1

β(n, i)yk,b

(m)k

(i)yk,b

(�)k

(i),

m, � = 1, 2, . . . , nk zk(n)nk �th

z(�)k (n) =

n∑i=1

β(n, i)ek(i)yk,b

(�)k

(i),

� = 1, 2, . . . , nk

ck(n) = P−1k (n)zk(n).

Pk(n)

yk(n) = [yk,b

(1)k

(n) yk,b

(2)k

(n) · · · yk,b

(nk)

k

(n)]T

zk(n) yk(n)ek(n)

β(n, i)

β(n, i) =

{1, n−i < L0, n−i > L,

L

β(n, i) P(n)z(n)

P−1k (n)

β(n, i)

L = 500

Page 4: [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain (2012.09.23-2012.09.26)] 2012 IEEE International Workshop on Machine Learning

wk(−1) = ψk(−1) = 0 k = 1, 2, . . . , N.

n = 1, 2, . . .

k = 1, 2, . . . , N

yk(n) = uT

k (n)ψk(n− 1)ek(n) = dk(n)− yk(n)μk(n) = μk/(δ + ‖uk(n)‖2)ψk(n) = ψk(n− 1) + μk(n)uk(n)ek(n)

k = 1, 2, . . . , N

�, m = 1, 2, · · · , nk

yk,b

(m)k

(n) = uT

k (n)wb(m)k

(n− 1)

yk,�(n) = yk,�(n)− yk(n)

[Pk(n)]m� =

n∑i=1

β(n, i)yk,b

(m)k

(i)yk,b

(�)k

(i)

z(�)k (n) =

n∑i=1

β(n, i)ek(i)yk,b

(�)k

(i)

ck(n) = P−1k (n)zk(n)

k = 1, 2, . . . , N

wk(n) = [1− 1T ck(n)]ψk(n)

+

nk∑m=1

c(m)k (n)w

b(m)k

(n− 1)

wo

M = 32−1 1

n = 5000

uk(n), k = 1, . . . , 4,

I vk(n)uk(n)

μ1 = μ2 = μ4 = 1μ3 = 0.1

μk = 1 μk = 0.1

4(n) = E‖wo − w4(n)‖2

wo(n)

{wo(n) = wo + θ(n)θ(n) = γθ(n− 1) + q(n),

0 < γ < 1 q(n)Q

wo(n)γ = 0.99 Q = σ2

qI σq (Q) =0.01 Tr(·)

−4 −6

−8

μ3 = 0.1

Page 5: [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain (2012.09.23-2012.09.26)] 2012 IEEE International Workshop on Machine Learning

E‖w

o−ψ

k(n

)‖2

(dB

)

10

0

0

−10

−20

−30

−40

2000 4000 6000 8000 10000

E‖w

o−

w4(n

)‖2

(dB

)

10

0

0

−10

−20

−30

−40

2000 4000 6000 8000 10000

0

0 2000 4000 6000 8000 10000

0.20.40.60.8

0.20.4

1.01.2 c14 c24 c34 c44

00 2000 4000 6000 8000 10000

0.2

0.4

0.6

0.8

1.0

1.2

c14 c24 c34 c44

M = 50

μk = 0.1μk = 1

wk(n) = ckk(n)ψk(n) +

nk∑m=1

c(m)k (n)ψ

b(m)k

(n).

yk,p(n)

0

0 500 1000 1500 2000 2500 3000

−8

−6

−4

−2

2

4

6

E‖w

o(n

)−w

4(n

)‖2

(dB

)

μk = 1 μk = 0.1

yk,p(n)Pk(n)

wo(n) (Q) = 10−1

(Q) = 10−3 (Q) = 10−5

μk = 0.1

Page 6: [IEEE 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain (2012.09.23-2012.09.26)] 2012 IEEE International Workshop on Machine Learning

−50

−40

−30

−20

−10

0

20

10

500 1000 1500 2000 2500 3000 3500 4000 4500 5000

(Q)

500 1000 1500 2000 2500 3000 3500 40005

6

7

8

9

11

12

13

10

500 1000 1500 2000 2500 3000 3500 4000−20

−15

−5

−10

0

5

15

10

500 1000 1500 2000 2500 3000 3500 4000−40

−30

−20

−10

0

20

10

(Q) = 10−1 (Q) = 10−3 (Q) = 10−5