COS-LDL: Label Distribution Learning by Cosine-Based Distance-Mapping Correlation

Label distribution learning (LDL) is a popular research trend in multi-label learning. Competing methods have been designed to improve the predictive performance. In this paper, we propose a method called cosine-based correlation for LDL (COS-LDL). The key issue is how to exploit correlations among different labels of the same instance. We propose a distance-mapping function for this purpose. With this mapping function, we design an objective function and its corresponding learning algorithm. Experiments undertaken on thirteen real-world datasets compare with eight LDL state-of-the-art methods. Results demonstrate that COS-LDL outperforms them in eight out of ten popular measures.

[1]  T. Sørensen,et al.  A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .

[2]  P. Danielsson.  Euclidean distance mapping , 1980 .

[3]  Ya-Xiang Yuan,et al.  A modified BFGS algorithm for unconstrained optimization , 1991 .

[4]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[5]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[6]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[7]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[8]  D. Gavin,et al.  A statistical approach to evaluating distance metrics and analog assignments for pollen records , 2003, Quaternary Research.

[9]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[10]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[11]  Cordelia Schmid,et al.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.

[12]  Kun Zhang,et al.  Multi-label learning by exploiting label dependency , 2010, KDD.

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[14]  Zhi-Hua Zhou,et al.  Multi-Label Learning by Exploiting Label Correlations Locally , 2012, AAAI.

[15]  Zhi-Hua Zhou,et al.  Facial Age Estimation by Learning from Label Distributions , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Xin Geng,et al.  Label Distribution Learning , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[17]  Xin Geng,et al.  Facial Age Estimation by Adaptive Label Distribution Learning , 2014, 2014 22nd International Conference on Pattern Recognition.

[18]  James T. Kwok,et al.  Multilabel Classification with Label Correlations and Missing Labels , 2014, AAAI.

[19]  Xiu-Shen Wei,et al.  Deep Label Distribution Learning for Apparent Age Estimation , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[20]  Xin Geng,et al.  Crowd counting in public video surveillance by label distribution learning , 2015, Neurocomputing.

[21]  Xuan Zhang,et al.  Emotion Distribution Learning from Texts , 2016, EMNLP.

[22]  Kai Zhao,et al.  Label Distribution Learning Forests , 2017, NIPS.

[23]  Xin Geng,et al.  Soft video parsing by label distribution learning , 2018, Frontiers of Computer Science.

[24]  Xiuyi Jia,et al.  Label Distribution Learning by Exploiting Sample Correlations Locally , 2018, AAAI.

[25]  Yu Zhang,et al.  Label Distribution Learning by Exploiting Label Correlations , 2018, AAAI.

[26]  Tao Wang,et al.  Partial Multi-Label Learning by Low-Rank and Sparse Decomposition , 2019, AAAI.

[27]  Shaohua Li,et al.  Facial Pose Estimation by Deep Learning from Label Distributions , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[28]  Xin Geng,et al.  Hierarchical Classification Based on Label Distribution Learning , 2019, AAAI.

[29]  Greg Mori,et al.  Learning a Deep ConvNet for Multi-Label Classification With Partial Labels , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Weiwei Li,et al.  Label distribution learning with label-specific features , 2019, IJCAI.

[31]  Zechao Li,et al.  Facial Emotion Distribution Learning by Exploiting Low-Rank Label Correlations Locally , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Shen Furao,et al.  Latent Semantics Encoding for Label Distribution Learning , 2019, IJCAI.

[33]  Jing Wang,et al.  Classification with Label Distribution Learning , 2019, IJCAI.

[34]  Xin Geng,et al.  Soft Facial Landmark Detection by Label Distribution Learning , 2019, AAAI.

[35]  Weiwei Li,et al.  Label Distribution Learning with Label Correlations via Low-Rank Approximation , 2019, IJCAI.

[36]  Sebastián Ventura,et al.  Distributed multi-label feature selection using individual mutual information measures , 2020, Knowl. Based Syst..

[37]  Yan Wang,et al.  Deep Differentiable Random Forests for Age Estimation , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.