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Learning hash functions/codes for similarity search over multi-view data isattracting increasing attention, where similar hash codes are assigned to thedata objects characterizing consistently neighborhood relationship acrossviews. Traditional methods in this category inherently suffer threelimitations: 1) they commonly adopt a two-stage scheme where similarity matrixis first constructed, followed by a subsequent hash function learning; 2) thesemethods are commonly developed on the assumption that data samples withmultiple representations are noise-free,which is not practical in real-lifeapplications; 3) they often incur cumbersome training model caused by theneighborhood graph construction using all $N$ points in the database ($O(N)$).In this paper, we motivate the problem of jointly and efficiently training therobust hash functions over data objects with multi-feature representationswhich may be noise corrupted. To achieve both the robustness and trainingefficiency, we propose an approach to effectively and efficiently learninglow-rank kernelized \footnote{We use kernelized similarity rather than kernel,as it is not a squared symmetric matrix for data-landmark affinity matrix.}hash functions shared across views. Specifically, we utilize landmark graphs toconstruct tractable similarity matrices in multi-views to automaticallydiscover neighborhood structure in the data. To learn robust hash functions, alatent low-rank kernel function is used to construct hash functions in order toaccommodate linearly inseparable data. In particular, a latent kernelizedsimilarity matrix is recovered by rank minimization on multiple kernel-basedsimilarity matrices. Extensive experiments on real-world multi-view datasetsvalidate the efficacy of our method in the presence of error corruptions.

Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions