Multiple-Instance Learning with Instance Selection via Dominant Sets

Erdem, Aykut; Erdem, Erkut

doi:10.1007/978-3-642-24471-1_13

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…The averaging operation mitigates the effects of positive instance in negative bags. In [94], robustness to label noise is obtained by using dominant sets to perform clustering and select relevant instance prototype in a bag-embedding algorithm similar to MILES [4].…”

Section: Label Noisementioning

confidence: 99%

Multiple instance learning: A survey of problem characteristics and applications

Carbonneau¹,

Cheplygina²,

Granger³

et al. 2018

Pattern Recognition

545

313

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Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.

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Section: Label Noisementioning

confidence: 99%

Multiple instance learning: A survey of problem characteristics and applications

Carbonneau¹,

Cheplygina²,

Granger³

et al. 2018

Pattern Recognition

545

313

View full text Add to dashboard Cite

show abstract

“…Such methods include clustering the prototypes and then selecting the cluster centers as prototypes. In [20] and [21], clustering of instances is performed. Erdem and Erdem [20] first rely on the standard assumption to cluster and prune the instances of negative bags, creating negative prototypes.…”

Section: B Classifier and Informative Prototypesmentioning

confidence: 99%

“…In [20] and [21], clustering of instances is performed. Erdem and Erdem [20] first rely on the standard assumption to cluster and prune the instances of negative bags, creating negative prototypes. The positive prototypes are created by selecting from each bag the instance that is furthest from the negative prototypes.…”

Section: B Classifier and Informative Prototypesmentioning

confidence: 99%

Dissimilarity-Based Ensembles for Multiple Instance Learning

Cheplygina¹,

Tax²,

Loog³

2016

IEEE Trans. Neural Netw. Learning Syst.

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In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper, we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation, determined by the number of training bags, whereas the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. However, an advantage of the latter representation is that the informativeness of the prototype instances can be inferred. In this paper, a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide insight into the structure of some popular multiple instance problems and show state-of-the-art performances on these data sets.

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“…Multiple instance learning (MIL), i.e., learning from ambiguous data (the labels are related to bags, not instances within the bags, meaning that we only have partial or incomplete knowledge about training instances), has been widely studied and applied to many challenging tasks, such as text categorization [1], object tracking [2], person re-identification [3], computer-aided medical diagnosis [4], etc. Therefore, it has received considerable attention, and various algorithms, for example APR [5], DD [6], Citation-KNN [7], EM-DD [8], MI-Kernel [9], miSVM and MISVM [10], DD-SVM [11], MILES [12], MissSVM [13], MIGraph and miGraph [14], MILIS [15], MILDS [16], MILEAGE [17], mi-DS [18], CK_MIL [19], SMILE [20], MIKI [21], TreeMIL [22], MILDM [23], mi-Net and MI-Net [24], Attention and Gated-Attention MIL [25], etc., have been proposed to deal with the MIL problem. However, there are two issues that hinder its practical application.…”

Section: Introductionmentioning

confidence: 99%

MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

Wen

Zhan

et al. 2019

Symmetry

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As a subject area of symmetry, multiple instance learning (MIL) is a special form of a weakly supervised learning problem where the label is related to the bag, not the instances contained in it. The difficulty of MIL lies in the incomplete label information of instances. To resolve this problem, in this paper, we propose a novel diverse density (DD) and multiple part similarity combination method for multiple instance learning, named MILDMS. First, we model the target concepts optimization with a DD function constraint on positive and negative instance space, which can greatly improve the robustness to label noise problem. Next, we combine the positive and negative instances in the bag (generated by hand-crafted and convolutional neural network features) with multiple part similarities to construct an MIL kernel. We evaluate the proposed approach on the MUSK dataset, whose results MUSK1 (91.9%) and MUSK2 (92.2%) show our method is comparable to other MIL algorithms. To further demonstrate generality, we also present experimental results on the PASCAL VOC 2007 and 2012 (46.5% and 42.2%) and COREL (78.6%) that significantly outperforms the state-of-the-art algorithms including deep MIL and other non-deep MIL algorithms.

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Multiple-Instance Learning with Instance Selection via Dominant Sets

Cited by 15 publications

References 18 publications

Multiple instance learning: A survey of problem characteristics and applications

Multiple instance learning: A survey of problem characteristics and applications

Dissimilarity-Based Ensembles for Multiple Instance Learning

MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

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