text_combined: irina rish dwtcusibmcom call papers novel applications dimensionality reduction httpwwwseasupennedukilianwnldrworkshop06 workshop held 20th annual conference neural information processing systems nips 2006 whistler canada december 8 2006 dimensionality reduction important research topic machine learning motivated several needs including data visualization representation discovering meaningful underlying structures reducing computational complexity improving accuracy avoiding overfitting due data sparsity past years significant progress made area including development novel algorithms nonlinear dimensionality reduction isomap locally linear embedding local tangent space alignment maximum variance unfolding etc supervised dimensionality reduction neighborhood components analysis maxmargin matrix factorization support vector decomposition etc taken significant steps toward overcoming deficiencies traditional linear methods like pca fishers lda time growing interest applying novel dimensionality reduction techniques wide range practical domains including robotics image driven navigation localization neuroscience biomedical imaging face recognition bioinformatics natural language processing name typically applications highdimensional state actionspaces discovering lowdimensional structures spaces improve understanding domain well efficiency learning decisionmaking given developments believe time revisit dimensionality reduction techniques point view various practical applications specific goals objective workshop understand match capabilities new nonlinear supervised dimensionality reduction approaches practical applications science engineering technology hope achieve bringing together researchers develop techniques apply successful workshop lead new directions applicationoriented dimensionalityreduction research ignite crossfertilization different application domains workshop address following questions characterize application domains amenable nonlinear andor supervised dimensionality reduction characterize methods best suited specific application domains given application domain properties data must preserved lowdimensional representation application goal prediction decisionmaking used influence dimensionality reduction process suggested topics would welcome submissions applications dimensionality reduction particularly novel nonlinear andor supervised methods various practical domains including limited mentioned encourage case studies approaches improve results accuracy performance lead better understanding underlying problem structure would also encourage analyses comparisons various approaches specific application scenarios format proposing oneday workshop planning one tutorial 4 invited talks shorter contributions researches industry academia well panel discussion presentation followed 1015 minutes discussion aspects detailed earlier overview hold poster session receive sufficient number good submissions workshop intended accessible broader nips community encourage communication different fields submission instructions invite submissions extended abstracts 2 pages including bibliography short contributed talks andor posters submission present highlevel description recent ongoing work related topics explore possibility publishing papers based invited submitted talks special issue appropriate journal email submissions qqij65qveisiuuwatsonibmcom attachments postscript pdf later november 3 2006 information workshop url httpwwwseasupennedukilianwnldrworkshop06 submission qqij65qveisiuuwatsonibmcom nips httpwwwnipscc dates deadlines november 3 abstract submission november 8 acceptance notification organizing committee john blitzer university pennsylvania usa rajarshi das ibm j watson research lab usa irina rish ibm j watson research lab usa kilian weinberger chair university pennsylvania usa invited speakers tba see attached file nips06nldrworkshopcfppdfsee attached file nips06nldrworkshopcfpdoc thu 07 aug 2008 063549 0400 uai nips 2006 workshop novel applications dimensionality reduction