Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval

Mohamed, Hela Haj; Belaid, Samir

doi:10.1016/j.neucom.2015.05.045

Cited by 15 publications

(1 citation statement)

References 39 publications

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“…To obtain a good performance in the test process, the problem of learning a classifier to directly optimize the same multivariate performance measure over the training set is proposed [1,2,3,4,5,6,7,8,9,10,11,12,13]. Meanwhile, the key of representing multi-instance data is the learning of a multiinstance dictionary, which is used to map a bag to a bag-level feature vector [14,15,16,17,18,19,20,16,21,22,23,24]. We observe that different performance measures require different optimal multi-instance dictionaries, i.e., the optimal performance is dependent on the optimal dictionary.…”

Section: Introductionmentioning

confidence: 99%

Multi-instance dictionary learning via multivariate performance measure optimization

Wang

Tsang

Cui

et al. 2017

Pattern Recognition

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The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F 1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized

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