Học phân phối nhãn không đầy đủ dựa trên thông tin láng giềng có giám sát

Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734–1748 Geng X, Yin C, Zhou ZH (2013) Facial age estimation by learning from label distributions. IEEE Trans Patt Anal Mach Intel 35(10):2401–2412 Geng X, Smith-Miles K, Zhou ZH (2010) Facial age estimation by learning from label distributions. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 451–456 Geng X, Hou P (2015) Pre-release prediction of crowd opinion on movies by label distribution learning. In: Proceedings of the 24th international conference on artificial intelligence (ICAI), pp 3511–3517 Zhou Y, Xue H, Geng X (2015) Emotion distribution recognition from facial expressions. In: Proceedings of the 23rd annual ACM conference on multimedia (ACM MM), pp 1247–1250 Geng X, Xia Y (2014) Head pose estimation based on multivariate label distribution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition (CVPR), pp 1837–1842 Yang X, Gao BB, Xing C et al (2015) Deep label distribution learning for apparent age estimation. In: Proceedings of the IEEE international conference on computer vision workshops, pp 344–350 Zhang Z, Wang M, Geng X (2015) Crowd counting in public video surveillance by label distribution learning. Neurocomputing 166:151–163 Xing C, Geng X, Xue H (2016) Logistic boosting regression for label distribution learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4489–4497 Gao BB, Xing C, Xie CW et al (2017) Deep label distribution learning with label ambiguity. IEEE Trans Image Process 26(6):2825–2838 Ren Y, Geng X (2017) Sense beauty by label distribution learning. In: Proceedings of the 26th international joint conference on artificial intelligence (IJCAI), pp 2648–2654 Shen W, Zhao K, Guo Y et al (2017) Label distribution learning forests. In: Advances in neural information processing systems (NIPS), pp 834–843 Chen M, Wang X, Feng B et al (2018) Structured random forest for label distribution learning. Neurocomputing 320:171–182 Jia XY, Li WW, Liu JY et al (2018) Label distribution learning by exploiting label correlations. In: Proceedings of the 32nd AAAI conference on artificial intelligence (AAAI), pp 3310–3317 Zhao P, Zhou ZH (2018) Label distribution learning by optimal transport. In: Proceedings of the 32nd AAAI conference on artificial intelligence (AAAI), pp 4506–4513 Tanha J, van Someren M, Afsarmanesh H (2017) Semi-supervised self-training for decision tree classifiers. Int J Mach Learn Cybern 8(1):355–370 Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27 Jiang L, Cai Z, Wang D et al (2014) Bayesian citation-KNN with distance weighting. Int J Mach Learn Cybern 5(2):193–199 Wang Y, Xu XY, Zhao HF et al (2010) Semi-supervised learning based on nearest neighbor rule and cut edges. Knowl Based Syst 23(6):547–554 Shieh HL (2012) Semi-supervised clustering based on k-nearest neighbors. In: Proceedings of the 2012 third international conference on digital manufacturing and automation (ICDMA), pp 759–762 Herlocker J, Konstan JA, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr 5(4):287–310 Bell RM, Koren Y (2007) Improved neighborhood-based collaborative filtering. In: KDD cup and workshop at the 13th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 7–14 Boulesteix AL (2004) PLS dimension reduction for classification with microarray data. Stat Appl Genet Mol Biol 3(1):1–30 Pietra SD, Pietra VD, Lafferty J (1997) Inducing features of random fields. IEEE Trans Patt Anal Mach Intel 19(4):380–393 Wright S, Nocedal J (1999) Numerical optimization. Springer Sci 35(7):67–68 Hullermeier E, Furnkranz J, Cheng W et al (2008) Label ranking by learning pairwise preferences. Artif Intell 172(16–17):1897–1916 Zhou Y, Qiu G (2018) Random forest for label ranking. Expert Syst Appl 112:99–109 Xu M, Zhou ZH (2017) Incomplete label distribution learning. In: Proceedings of the 26th international joint conference on artificial intelligence (IJCAI), pp 3175–3181 Bertsekas DP (2011) Incremental gradient, subgradient, and proximal methods for convex optimization: a survey. In: Suvrit SRA, Sebastian N, Stephen JW (eds) Optimization for machine learning. The MIT Press, Cambridge, pp 85–119 Boyd S, Parikh N, Chu E et al (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122 Zeng XQ, Li GZ (2014) Incremental partial least squares analysis of big streaming data. Pattern Recogn 47(11):3726–3735 Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2(1):97–106 Geladi P, Kowalski BR (1985) Partial least-squares regression: a tutorial. Anal Chim Acta 185(86):1–17 Abdi H, Lynne JW (2013) Partial least squares methods: partial least squares correlation and partial least square regression. In: Brad R, Arthur NM (eds) Comput toxicol. Humana Press, New York, pp 549–579 Mehmood T, Bilal A (2016) The diversity in the applications of partial least squares: an overview. J Chemom 30(1):4–17. https://doi.org/10.1007/978-1-62703-059-5_23 Liu H, Ma Z, Han J et al (2018) Regularized partial least squares for multi-label learning. Int J Mach Learn Cybern 9(2):335–346