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1. A Manifold Laplacian Regularized Semi-supervised Sparse Image Classification Method with a Variant Trace Lasso Norm

   https://doi.org/DOI: 10.1109/ACCESS.2020.2997413  

   Zhang, Wenjuan; Feng, Xiangchu; Chen, Yunmei (May 2020, IEEE access)

   Since the cost of labeling data is getting higher and higher, we hope to make full use of the large amount of unlabeled data and improve image classification effect through adding some unlabeled samples for training. In addition, we expect to uniformly realize two tasks, namely the clustering of the unlabeled data and the recognition of the query image. We achieve the goal by designing a novel sparse model based on manifold assumption, which has been proved to work well in many tasks. Based on the assumption that images of the same class lie on a sub-manifold and an image can be approximately represented as the linear combination of its neighboring data due to the local linear property of manifold, we proposed a sparse representation model on manifold. Specifically, there are two regularizations, i.e., a variant Trace lasso norm and the manifold Laplacian regularization. The first regularization term enables the representation coefficients satisfying sparsity between groups and density within a group. And the second term is manifold Laplacian regularization by which label can be accurately propagated from labeled data to unlabeled data. Augmented Lagrange Multiplier (ALM) scheme and Gauss Seidel Alternating Direction Method of Multiplier (GS-ADMM) are given to solve the problem numerically. We conduct some experiments on three human face databases and compare the proposed work with several state-of-the-art methods. For each subject, some labeled face images are randomly chosen for training for those supervised methods, and a small amount of unlabeled images are added to form the training set of the proposed approach. All experiments show our method can get better classification results due to the addition of unlabeled samples. 

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2. Deep learning approach to multimedia traffic classification based on QoS characteristics

   https://doi.org/10.1049/iet-net.2018.5179  

   Wang, Zaijian; Mao, Shiwen; Yang, Weidong (May 2019, IET Networks)

   With the fast increase of multimedia traffic in Internet of Things (IoT) applications, IoT traffic now requires very different Quality of Service (QoS). By extensive statistical analysis of traffic flow data from a real world network, the authors find that there are some latent features hidden in the multimedia data, which can be useful for accurately differentiating multimedia traffic flows from the QoS perspective. Under limited training data conditions, existing shallow classification methods are limited in performance, and are thus not effective in classifying emerging multimedia traffic types, which have truly entered the era of big data and become very completed in QoS features. This situation inspires us to revisit the multimedia traffic classification problem with a deep learning (DL) approach. In this study, an improved DL‐based multimedia traffic classification method is proposed, which considers the inherent structure of QoS features in multimedia data. An improved stacked autoencoder model is employed to learn the relevant QoS features of multimedia traffic. Extensive experimental studies with multimedia datasets captured from a campus network demonstrate the effectiveness of the proposed method over six benchmark schemes. 

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3. Confidence Estimation Using Unlabeled Data

   Li, Chen; Hu, Xiaoling; Chen, Chao (May 2023, International Conference on Learning Representations)

   Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task. 

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4. Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction

   Zhang, W; Hong, B; Ye, J; Cai, D; He, X; Wang, J. (January 2017, Proceedings of the 34th International Conference on Machine Learning)

   Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVMs remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the first static feature and sample reduction method for sparse SVMs. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude. 

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5. Weakly-Supervised Neural Text Classification

   https://doi.org/10.1145/3269206.3271737  

   Meng, Yu; Shen, Jiaming; Zhang, Chao; Han, Jiawei (October 2018, Proceedings of the 27th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2018)

   Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semisupervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly. 

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