Joint Structural Learning to Rank with Deep Linear Feature Learning

Multimedia information retrieval usually involves two key modules including effective feature representation and ranking model construction. Most existing approaches are incapable of well modeling the inherent correlations and interactions between them, resulting in the loss of the latent consensus structure information. To alleviate this problem, we propose a learning to rank approach that simultaneously obtains a set of deep linear features and constructs structure-aware ranking models in a joint learning framework. Specifically, the deep linear feature learning corresponds to a series of matrix factorization tasks in a hierarchical manner, while the learning-to-rank part concentrates on building a ranking model that effectively encodes the intrinsic ranking information by structural SVM learning. Through a joint learning mechanism, the two parts are mutually reinforced in our approach, and meanwhile their underlying interaction relationships are implicitly reflected by solving an alternating optimization problem. Due to the intrinsic correlations among different queries (i.e., similar queries for similar ranking lists), we further formulate the learning-to-rank problem as a multi-task problem, which is associated with a set of mutually related query-specific learning-to-rank subproblems. For computational efficiency and scalability, we design a MapReduce-based parallelization approach to speed up the learning processes. Experimental results demonstrate the efficiency, effectiveness, and scalability of the proposed approach in multimedia information retrieval.

[1]  Tie-Yan Liu,et al.  Listwise approach to learning to rank: theory and algorithm , 2008, ICML '08.

[2]  Yu-Jin Zhang,et al.  Nonnegative Matrix Factorization: A Comprehensive Review , 2013, IEEE Transactions on Knowledge and Data Engineering.

[3]  Hanqing Lu,et al.  Random subspace for binary codes learning in large scale image retrieval , 2014, SIGIR.

[4]  Chongyu Chen,et al.  Surveillance video coding via low-rank and sparse decomposition , 2012, ACM Multimedia.

[5]  Shichao Zhang,et al.  Robust Perceptual Image Hashing Based on Ring Partition and NMF , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Hans-Peter Kriegel,et al.  Scalable Probabilistic Similarity Ranking in Uncertain Databases , 2010, IEEE Transactions on Knowledge and Data Engineering.

[7]  Xian-Sheng Hua,et al.  A unified context model for web image retrieval , 2012, TOMCCAP.

[8]  Tie-Yan Liu,et al.  Adapting ranking SVM to document retrieval , 2006, SIGIR.

[9]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[10]  Yanjun Qi,et al.  Learning to rank with (a lot of) word features , 2010, Information Retrieval.

[11]  Gert R. G. Lanckriet,et al.  Metric Learning to Rank , 2010, ICML.

[12]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[13]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[14]  Xian-Sheng Hua,et al.  Ranking Model Adaptation for Domain-Specific Search , 2009, IEEE Transactions on Knowledge and Data Engineering.

[15]  Jing Liu,et al.  Low rank metric learning for social image retrieval , 2012, ACM Multimedia.

[16]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[17]  John D. Lafferty,et al.  Document Language Models, Query Models, and Risk Minimization for Information Retrieval , 2001, SIGIR Forum.

[18]  Chunyan Miao,et al.  Online multimodal deep similarity learning with application to image retrieval , 2013, ACM Multimedia.

[19]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[20]  Bingbing Ni,et al.  Geometric ℓp-norm feature pooling for image classification , 2011, CVPR 2011.

[21]  Larry P. Heck,et al.  Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.

[22]  Graham W. Taylor,et al.  Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Jian Yang,et al.  Robust sparse coding for face recognition , 2011, CVPR 2011.

[24]  Qiang Zhou,et al.  Learning to Share Latent Tasks for Action Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[25]  Chih-Jen Lin,et al.  Projected Gradient Methods for Nonnegative Matrix Factorization , 2007, Neural Computation.

[26]  Filip Radlinski,et al.  A support vector method for optimizing average precision , 2007, SIGIR.

[27]  Hang Li,et al.  AdaRank: a boosting algorithm for information retrieval , 2007, SIGIR.

[28]  Andrew Y. Ng,et al.  Learning Feature Representations with K-Means , 2012, Neural Networks: Tricks of the Trade.

[29]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..

[30]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[31]  Yi-Hsuan Yang,et al.  On sparse and low-rank matrix decomposition for singing voice separation , 2012, ACM Multimedia.

[32]  S. Yun,et al.  An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems , 2009 .

[33]  Zhuowen Tu,et al.  Deeply-Supervised Nets , 2014, AISTATS.

[34]  André Stuhlsatz,et al.  Feature Extraction With Deep Neural Networks by a Generalized Discriminant Analysis , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[36]  Jing Xiao,et al.  Non-negative matrix factorization as a feature selection tool for maximum margin classifiers , 2011, CVPR 2011.

[37]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[38]  Xuelong Li,et al.  Constrained Nonnegative Matrix Factorization for Image Representation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Chiranjib Bhattacharyya,et al.  Structured learning for non-smooth ranking losses , 2008, KDD.

[40]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[41]  Changsheng Xu,et al.  Low-Rank Sparse Coding for Image Classification , 2013, 2013 IEEE International Conference on Computer Vision.

[42]  Kunle Olukotun,et al.  Map-Reduce for Machine Learning on Multicore , 2006, NIPS.

[43]  Zhongfei Zhang,et al.  Distributed cross-media multiple binary subspace learning , 2015, International Journal of Multimedia Information Retrieval.

[44]  Chun Chen,et al.  EMR: A Scalable Graph-Based Ranking Model for Content-Based Image Retrieval , 2015, IEEE Transactions on Knowledge and Data Engineering.

[45]  Stephen E. Robertson,et al.  The TREC-8 Filtering Track Final Report , 1999, TREC.

[46]  Zhongfei Zhang,et al.  Simultaneously Combining Multi-view Multi-label Learning with Maximum Margin Classification , 2012, 2012 IEEE 12th International Conference on Data Mining.

[47]  Yongsheng Ding,et al.  Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering , 2012, IEEE Transactions on Knowledge and Data Engineering.

[48]  Xuelong Li,et al.  A-Optimal Non-negative Projection for image representation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.