Mathematical Methodologies in Pattern Recognition and Machine Learning : Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012

200 Pages·2013·2.6 MB·English

by  Pedro Latorre Carmona

Springer Proceedings in Mathematics & Statistics
Volume 30
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Thisbookseriesfeaturesvolumescomposedofselectcontributionsfromworkshops
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well-edited, authoritative reports on developments in the most exciting areas of
mathematicalandstatisticalresearchtoday.
Pedro Latorre Carmona • J. Salvador Sa´nchez
Ana L.N. Fred
Editors
Mathematical Methodologies
in Pattern Recognition
and Machine Learning
Contributions from the International
Conference on Pattern Recognition
Applications and Methods, 2012
123
Editors
PedroLatorreCarmona J.SalvadorSa´nchez
Depto.LenguajesySistemasInforma´ticos Depto.LenguajesySistemasInforma´ticos
JaumeIUniversity JaumeIUniversity
Castello´ndelaPlana,Spain Castello´ndelaPlana,Spain
AnaL.N.Fred
TechnicalUniversityofLisbon
Lisbon,Portugal
ISSN2194-1009 ISSN2194-1017 (electronic)
ISBN978-1-4614-5075-7 ISBN978-1-4614-5076-4(eBook)
DOI10.1007/978-1-4614-5076-4
SpringerNewYorkHeidelbergDordrechtLondon
LibraryofCongressControlNumber:2012952083
MathematicsSubjectClassification(2010):93-XX,90-99,68-06
©SpringerScience+BusinessMediaNewYork2013
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Preface
This volume features key contributions from the International Conference on
PatternRecognitionApplicationsandMethods(ICPRAM2012)heldinVilamoura,
Algarve,PortugalfromFebruary6to8,2012.
ICPRAM was sponsored by the Institute for Systems and Technologies of
Information Control and Communication (INSTICC) and held in cooperation
with the Association for the Advancement of Artificial Intelligence (AAAI) and
Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL2).
It was technically co-sponsored by IEEE Signal Processing Society, Machine
Learning for Signal Processing (MLSP) Technical Committee of IEEE, AERFAI
(Asociacio´n Espan˜ola de Reconocimiento de Formas y Ana´lisis de Imagen) and
APRP(Associac¸a˜oPortuguesadeReconhecimentodePadro˜es).
ICPRAM received 259 paper submissions from 46 countries in all continents.
To evaluate each submission, a double-blind paper review was performed by the
ProgramCommittee,whosemembersarehighlyqualifiedresearchersinICPRAM
topicareas.Basedontheclassificationsprovided,only115paperswereselectedfor
oralpresentation(61fullpapersand54shortpapers)and32paperswereselected
forposterpresentation.Thefullpaperacceptanceratiowas24%,andthetotaloral
acceptance ratio (including full papers and short papers) was 44%. These strict
acceptance ratios show the intention to preserve a high quality forum which we
expecttodevelopfurthernextyear.
The conference provided a major point of collaboration between researchers,
engineersandpractitionersintheareasofPatternRecognition,bothfromtheoretical
and applied perspectives. Contributionsdescribed applications of pattern recogni-
tiontechniquestoreal-worldproblems,interdisciplinaryresearch,andexperimental
andtheoreticalstudies.
This book will be suitable for scientists and researchers in optimization, nu-
merical methods, computer science, statistics and for differential geometers and
mathematicalphysicists.
Castello´ndelaPlana,Spain PedroLatorreCarmona
Castello´ndelaPlana,Spain J.SalvadorSa´nchez
Lisbon,Portugal AnaL.N.Fred
v

Contents
OntheExpressivityofAlignment-BasedDistance
andSimilarityMeasuresonSequencesandTreesinInducing
Orderings ......................................................................... 1
MartinEmmsandHector-HugoFranco-Penya
AutomaticAnnotationofaDynamicCorpusbyLabelPropagation....... 19
ThomasLansdall-Welfare,IliasFlaounas,andNelloCristianini
ComputingVoronoiAdjacenciesinHighDimensionalSpaces
byUsingLinearProgramming................................................. 33
JuanMendezandJavierLorenzo
Phase-Locked Matrix Factorization with Estimation
oftheCommonOscillation...................................................... 51
MiguelAlmeida,RicardoViga´rio,andJose´ Bioucas-Dias
StochasticSubgradientEstimationTrainingforSupport
VectorMachines.................................................................. 67
SangkyunLeeandStephenJ.Wright
Single-FrameSignalRecoveryUsingaSimilarity-Prior .................... 83
SakinahA.PitchayandAtaKaba´n
ADiscretizedNewtonFlowforTime-VaryingLinear
InverseProblems................................................................. 99
MartinKleinsteuberandSimonHawe
ExploitingStructuralConsistencieswithStackedConditional
RandomFields.................................................................... 111
PeterKluegl,MartinToepfer,FlorianLemmerich,AndreasHotho,
andFrankPuppe
DetectingMean-RevertedPatternsinAlgorithmicPairsTrading.......... 127
K.TriantafyllopoulosandS.Han
vii
viii Contents
SegmentingCarotidinCTUsingGeometricPotentialField
DeformableModel ............................................................... 149
SiYongYeo,XianghuaXie,IgorSazonov,andPerumalNithiarasu
ARobustDeformableModelfor3DSegmentationoftheLeft
VentriclefromUltrasoundData................................................ 163
CarlosSantiago,JorgeS.Marques,andJacintoC.Nascimento
FacialExpressionRecognitionUsingDiffeomorphicImage
RegistrationFramework........................................................ 179
BartlomiejW.Papiez,BogdanJ.Matuszewski,Lik-KwanShark,
andWeiQuan
On the Expressivity of Alignment-Based
Distance and Similarity Measures on
Sequences and Trees in Inducing Orderings
MartinEmmsandHector-HugoFranco-Penya
Abstract Both ‘distance’ and ‘similarity’ measures have been proposed for the
comparison of sequences and for the comparison of trees, based on scoring
mappings. For a given alphabet of node-labels, the measures are parameterised
by a table giving label-dependentvalues for swaps, deletions and insertions. The
paper addresses the question whether an ordering by a ‘distance’ measure, with
someparametersetting,canbealsoexpressedbya‘similarity’measure,withsome
other parameter setting, and vice versa. Ordering of three kinds is considered:
alignment-orderings,for fixed source S and target T, neighbour-orderings,where
forafixedS,varyingcandidateneighboursT areranked,andpair-orderings,where
i
for varying S, and varying T , the pairings (cid:2)S,T (cid:3) are ranked. We show that
i j i j
(1) any alignment-ordering expressed by ‘distance’ setting be re-expressed by a
‘similarity’ setting, and vice versa; (2) any neigbour-ordering and pair-ordering
expressedbya‘distance’settingbere-expressedbya‘similarity’setting;(3)there
areneighbour-orderingsandpair-orderingsexpressedbya‘similarity’settingwhich
cannot be expressed by a ‘similarity’ setting. A consequence of this is that there
arecategorisationandhierarchicalclusteringoutcomeswhichcanbeachievedvia
similaritybutnotviadistance.
Keyword Similaritydistancetreesequence
M.Emms((cid:2))(cid:129)H.-H.Franco-Penya
School ofComputerScienceandStatistics,TrinityCollege,Dublin,Ireland
e-mail:[email protected];[email protected]
P. Latorre Carmonaetal.(eds.),MathematicalMethodologiesinPatternRecognition 1
andMachineLearning,SpringerProceedingsinMathematics&Statistics30,
DOI10.1007/978-1-4614-5076-4 1,©SpringerScience+BusinessMediaNewYork2013