Semi-supervised Learning For Classification Research Articles

An Image Classification Method Based on Semi-Supervised Classification Learning and Convolutional Neural Networks

This paper aims to propose an improved image classification model to reduce the cost of model construction. Aiming at the problem that network training usually requires the support of a large number of labeled samples, an image classification model based on semi-supervised deep learning is proposed, which uses labeled samples to guide the network to learn unlabeled samples. A convolutional neural network model for simultaneous processing of labeled and unlabeled data is constructed. The tagged data is used to train the Softmax classifier and provide the initial K-means clustering center for the untagged data. The nonsubsampling contourlet layer is used to replace the first convolutional layer of the full convolutional neural network to extract multi-scale depth features, and the nonsubsampling contourlet full convolutional neural network is constructed. The network can extract multi-scale information of the images to be classified, and extract more discriminative deep image features. In addition, the parameters of the nonsubsampled contourlet layers are pre-set and do not require network training. The proposed method has higher classification accuracy than the contrast method on polarimetric SAR images using the nonsubsampled contourlet full convolutional neural network.

Semi-supervised classification learning by discrimination-aware manifold regularization

Manifold regularization (MR) provides a powerful framework for semi-supervised classification (SSC) using both the labeled and unlabeled data. It first constructs a single Laplacian graph over the whole dataset for representing the manifold structure, and then enforces the smoothness constraint over such graph by a Laplacian regularizer in learning. However, the smoothness over such a single Laplacian graph may take the risk of ignoring the discrimination among boundary instances, which are very likely from different classes though highly close to each other on the manifold. To compensate for such deficiency, researches have already been devoted by taking into account the discrimination together with the smoothness in learning. However, those works are only confined to the discrimination of the labeled instances, thus rather limited in boosting the semi-supervised learning. To mitigate such an unfavorable situation, we attempt to discover the possible discrimination in the available instances first by performing some unsupervised clustering over the whole dataset, and then incorporate it into MR to develop a novel discrimination-aware manifold regularization (DAMR) framework. In DAMR, instances with high similarity on the manifold will be restricted to share the same class label if belonging to the same cluster, or to have different class labels, otherwise. Our empirical results show the competitiveness of DAMR compared to MR and its variants likewise incorporating the discrimination in learning.

Safety-aware semi-supervised classification.

Though semi-supervised classification learning has attracted great attention over past decades, semi-supervised classification methods may show worse performance than their supervised counterparts in some cases, consequently reducing their confidence in real applications. Naturally, it is desired to develop a safe semi-supervised classification method that never performs worse than the supervised counterparts. However, to the best of our knowledge, few researches have been devoted to safe semi-supervised classification. To address this problem, in this paper, we invent a safety-control mechanism for safe semi-supervised classification by adaptive tradeoff between semi-supervised and supervised classification in terms of unlabeled data. In implementation, based on our recent semi-supervised classification method based on class memberships (SSCCM), we develop a safety-aware SSCCM (SA-SSCCM). SA-SSCCM, on the one hand, exploits the unlabeled data to help learning (as SSCCM does) under the assumption that unlabeled data can help learning, and on the other hand, restricts its prediction to approach that of its supervised counterpart least-square support vector machine (LS-SVM) under the assumption that unlabeled data can hurt learning. Therefore, prediction by SA-SSCCM becomes a tradeoff between those by semi-supervised SSCCM and supervised LS-SVM, respectively, in terms of the unlabeled data. As in SSCCM, the optimization problem in SA-SSCCM can be efficiently solved by the alternating iterative strategy, and the iteration convergence can theoretically be guaranteed. Experiments over several real datasets show the promising performance of SA-SSCCM compared with LS-SVM, SSCCM, and off-the-shelf safe semi-supervised classification methods.

Semi-Supervised Learning for Classification with Uncertainty

Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning (S3VM) has attracted more and more attentions. In general, S3VM deals with problems with small training sets and large working sets. When the training set is large relative to the working set, We propose a new SVM model to solve the above classification problem by introducing the fuzzy memberships to each unlabeled point. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.

Biological Data Mining

Biological data mining purports to extract useful knowledge from massive datasets gathered in biology, and in other related life sciences areas such as medicine and neuroscience. In the recent past, we have witnessed major transformations of these applied sciences into data-driven endeavors. For instance, the study of even a focused aspect of cellular activity, such as gene action, now benefits from multiple highthroughput data acquisition technologies such as microarrays, genome-wide deletion screens, and RNAi assays. Consequently, analysis and mining techniques, especially those that provide data reduction down to manageable quantities, have become a mainstay of these application domains. This special issue presents novel research in biological data mining applications. The selected submissions went through two rounds of reviews by at least three reviewers. We are very grateful to the anonymous reviewers in helping us select the following papers for this special issue. The first paper, Semi-supervised learning for classification of protein sequence data, by Brian King and Chittibabu Guda, presents a com-