By Gabriel B. P. Costa, Moacir Ponti, Alejandro C. Frery (auth.), Zhi-Hua Zhou, Friedhelm Schwenker (eds.)
This booklet constitutes the completely refereed revised chosen papers from the second one IAPR foreign Workshop, PSL 2013, held in Nanjing, China, in might 2013. the ten papers integrated during this quantity have been rigorously reviewed and chosen from 26 submissions. in part supervised studying is a swiftly evolving sector of computer studying. It generalizes many varieties of studying paradigms together with supervised and unsupervised studying, semi-supervised studying for class and regression, transductive studying, semi-supervised clustering, multi-instance studying, vulnerable label studying, coverage studying in in part observable environments, etc.
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This publication constitutes the completely refereed revised chosen papers from the second one IAPR overseas Workshop, PSL 2013, held in Nanjing, China, in could 2013. the ten papers incorporated during this quantity have been conscientiously reviewed and chosen from 26 submissions. partly supervised studying is a quickly evolving quarter of computer studying.
Extra info for Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers
Combining Pattern Classiﬁers: Methods and Algorithms. WileyInterscience, New York (2004) 18. : Speech emotion recognition using an enhanced co-training algorithm. In: IEEE International Conference on Multimedia and Expo, pp. 999–1002 (2007) 19. : The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-speciﬁed expression. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2010), pp. 94–101 (2010) 20. : Dictionary learning for the sparse modelling of atrial ﬁbrillation in ecg signals.
In many real applications, the original data matrix only contains non-negative values such as the term frequency in document sets, the gray value of the image pixel and etc. In this paper, thus, we introduce non-negative matrix factorization (NMF) technique to model the loss function between the data samples and the given limited label information. The basic idea of NMF is to ﬁnd several non-negative bases from the original data and then linearly project data onto these bases . Meanwhile, the nonnegative constraint leads NMF to a parts-based representation of the sample, which makes NMF popular in many ﬁelds.
G. with diﬀerent scaling) while dissimilar signals are represented by combinations of diﬀerent dictionary atoms. For a classiﬁer, this is a favorable starting position. For future work, the applicability of this approach to non-periodical signals could be examined with possible modiﬁcations to the alignment procedure. g. respiration) or even a single dictionary from combined signals. 6 Conclusion This paper presented an approach for emotion recognition based on blood volume pulse signals using sparse representations.