Improved Semi-Supervised Non-Negative Matrix Factorization with Weighted Label Information

Lu, Linzhang; Jing, Wenjing

doi:10.2139/ssrn.4507872

Cited by 1 publication

(2 citation statements)

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“…Semisupervised multilabel learning falls into two categories. One category is transductive multilabel learning, which assumes testing instances are from unlabeled instances [28,29]. In [28], the authors assumed that the similarity in the label space is closely related to that in the feature space, and thus employed the similarity in the feature space to guide learning on missing label assignments, leading to a constrained non-negative matrix factorization optimization.…”

Section: Weak Labelsmentioning

confidence: 99%

“…One category is transductive multilabel learning, which assumes testing instances are from unlabeled instances [28,29]. In [28], the authors assumed that the similarity in the label space is closely related to that in the feature space, and thus employed the similarity in the feature space to guide learning on missing label assignments, leading to a constrained non-negative matrix factorization optimization. In [29], the authors formulated a transductive multilabel learning method as an optimization problem in estimating label concept compositions.…”

Section: Weak Labelsmentioning

confidence: 99%

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Progressive Training Technique with Weak-Label Boosting for Fine-Grained Classification on Unbalanced Training Data

et al. 2022

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In practical classification tasks, the sample distribution of the dataset is often unbalanced; for example, this is the case in a dataset that contains a massive quantity of samples with weak labels and for which concrete identification is unavailable. Even in samples with exact labels, the number of samples corresponding to many labels is small, resulting in difficulties in learning the concepts through a small number of labeled samples. In addition, there is always a small interclass variance and a large intraclass variance among categories. Weak labels, few-shot problems, and fine-grained analysis are the key challenges affecting the performance of the classification model. In this paper, we develop a progressive training technique to address the few-shot challenge, along with a weak-label boosting method, by considering all of the weak IDs as negative samples of every predefined ID in order to take full advantage of the more numerous weak-label data. We introduce an instance-aware hard ID mining strategy in the classification loss and then further develop the global and local feature-mapping loss to expand the decision margin. We entered the proposed method into the Kaggle competition, which aims to build an algorithm to identify individual humpback whales in images. With a few other common training tricks, the proposed approach won first place in the competition. All three problems (weak labels, few-shot problems, and fine-grained analysis) exist in the dataset used in the competition. Additionally, we applied our method to CUB-2011 and Cars-196, which are the most widely-used datasets for fine-grained visual categorization tasks, and achieved respective accuracies of 90.1% and 94.9%. This experiment shows that the proposed method achieves the optimal effect compared with other common baselines, and verifies the effectiveness of our method. Our solution has been made available as an open source project.

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Section: Weak Labelsmentioning

confidence: 99%