IEEE Trans Pattern Anal Mach Intell - Sparse Subspace Clustering: Algorithm, Theory, and Applications.

Tópicos

{ imag(1947) propos(1133) code(1026) }
{ problem(2511) optim(1539) algorithm(950) }
{ model(2656) set(1616) predict(1553) }
{ motion(1329) object(1292) video(1091) }
{ method(1969) cluster(1462) data(1082) }
{ method(984) reconstruct(947) comput(926) }
{ intervent(3218) particip(2042) group(1664) }
{ model(3404) distribut(989) bayesian(671) }
{ general(901) number(790) one(736) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ featur(3375) classif(2383) classifi(1994) }
{ cost(1906) reduc(1198) effect(832) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ featur(1941) imag(1645) propos(1176) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ method(2212) result(1239) propos(1039) }
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{ data(1737) use(1416) pattern(1282) }
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{ system(1976) rule(880) can(841) }
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{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
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{ learn(2355) train(1041) set(1003) }
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{ extract(1171) text(1153) clinic(932) }
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{ studi(1410) differ(1259) use(1210) }
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{ model(2341) predict(2261) use(1141) }
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{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose and study an algorithm, called Sparse Subspace Clustering, to cluster high-dimensional data points that lie in a union of low-dimensional subspaces. The key idea is that, among infinitely many possible representations of a data point in terms of other points, a sparse representation corresponds to selecting a few points that come from the same subspace. This motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data. As solving the sparse optimization program is NP-hard, we consider its convex relaxation and show that, under appropriate conditions on the arrangement of the subspaces and the distribution of the data, the proposed minimization program succeeds in recovering the desired sparse representations. The proposed algorithm does not require initialization, can be solved efficiently, and can handle data points near the intersections of subspaces. In addition, our algorithm can deal with data nuisances, such as noise, sparse outlying entries, and missing entries, directly by modifying the optimization program to incorporate the model of the data. We verify the effectiveness of the proposed algorithm through experiments on synthetic data as well as two real-world problems of motion segmentation and face clustering.

Resumo Limpo

propos studi algorithm call spars subspac cluster cluster highdimension data point lie union lowdimension subspac key idea among infinit mani possibl represent data point term point spars represent correspond select point come subspac motiv solv spars optim program whose solut use spectral cluster framework infer cluster data solv spars optim program nphard consid convex relax show appropri condit arrang subspac distribut data propos minim program succeed recov desir spars represent propos algorithm requir initi can solv effici can handl data point near intersect subspac addit algorithm can deal data nuisanc nois spars out entri miss entri direct modifi optim program incorpor model data verifi effect propos algorithm experi synthet data well two realworld problem motion segment face cluster

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