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Author Fadi Dornaika; Bogdan Raducanu edit  openurl
  Title (up) 3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application Type Journal
  Year 2008 Publication IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) Abbreviated Journal  
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  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ DoR2008d Serial 1109  
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Author A. Martinez; Jordi Vitria edit  openurl
  Title (up) A Development Plataform for Autonomous Agents. Type Journal Article
  Year 1995 Publication ASI–AA–95 – Practice and Future of Autonomous Agents. Abbreviated Journal  
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  Address Monte Verita, Switzerland.  
  Corporate Author Thesis  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MaV1995b Serial 123  
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Author Maria Vanrell; Jordi Vitria; Xavier Roca edit  openurl
  Title (up) A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Type Journal Article
  Year 1997 Publication Machine Vision and Applications Abbreviated Journal  
  Volume 9 Issue Pages 262–271  
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  Notes OR;ISE;CIC;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ VVR1997 Serial 35  
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Author Agata Lapedriza; Santiago Segui; David Masip; Jordi Vitria edit  openurl
  Title (up) A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning Type Journal
  Year 2008 Publication Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, Abbreviated Journal  
  Volume 11 Issue 3-4 Pages 299-308  
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  Publisher Springer Place of Publication Editor  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ LSM2008 Serial 996  
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Author Bogdan Raducanu; Fadi Dornaika edit   pdf
url  doi
openurl 
  Title (up) A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning Type Journal Article
  Year 2012 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 45 Issue 6 Pages 2432-2444  
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  Abstract IF= 2.61
IF=2.61 (2010)
In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other.

Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance.		  
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  Publisher Elsevier Place of Publication Editor  
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  Series Volume Series Issue Edition  
  ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes OR; MV Approved no  
  Call Number Admin @ si @ RaD2012a Serial 1884  
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