{"intvolume":"      6312","oa_version":"None","volume":6312,"date_created":"2018-12-11T12:05:12Z","publist_id":"2433","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publisher":"Springer","year":"2010","scopus_import":1,"quality_controlled":"1","title":"Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning","status":"public","alternative_title":["LNCS"],"type":"conference","doi":"10.1007/978-3-642-15552-9_41","page":"566 - 579","day":"10","author":[{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","last_name":"Lampert"},{"first_name":"Oliver","last_name":"Krömer","full_name":"Krömer, Oliver"}],"month":"11","date_updated":"2021-01-12T07:52:14Z","publication_status":"published","conference":{"location":"Heraklion, Crete, Greece","end_date":"2010-09-11","name":"ECCV: European Conference on Computer Vision","start_date":"2010-09-05"},"date_published":"2010-11-10T00:00:00Z","citation":{"ama":"Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579. doi:<a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">10.1007/978-3-642-15552-9_41</a>","ista":"Lampert C, Krömer O. 2010. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. ECCV: European Conference on Computer Vision, LNCS, vol. 6312, 566–579.","chicago":"Lampert, Christoph, and Oliver Krömer. “Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning,” 6312:566–79. Springer, 2010. <a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">https://doi.org/10.1007/978-3-642-15552-9_41</a>.","apa":"Lampert, C., &#38; Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. <a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">https://doi.org/10.1007/978-3-642-15552-9_41</a>","short":"C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.","mla":"Lampert, Christoph, and Oliver Krömer. <i>Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning</i>. Vol. 6312, Springer, 2010, pp. 566–79, doi:<a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">10.1007/978-3-642-15552-9_41</a>.","ieee":"C. Lampert and O. Krömer, “Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6312, pp. 566–579."},"language":[{"iso":"eng"}],"department":[{"_id":"ChLa"}],"_id":"3794","main_file_link":[{"url":"http://www.ics.forth.gr/eccv2010/intro.php"}],"abstract":[{"text":"We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.","lang":"eng"}]}