Cross-modal hashing has achieved significant progress in recent years. However, how to effectively learn more discriminative hash codes of each modality and simultaneous alleviate the loss of modality information is still a challenging problem. Focusing on this problem, in this paper, we propose a novel cross-modal hashing approach named Semantic Preserving Generative Adversarial Network (SPGAN). The overall network architecture consists of two sub-networks, i.e., a semantic preserving generative adversarial network module and a discriminative hashing module. The generator maps text features into the image feature space. And the discriminator judges whether the feature representations are real image features or generated image features. The adversarial learning process can effectively reduce modality difference and preserve information of the image modality as much as possible. The discriminative hashing module projects the real and generated image features into a Hamming space to obtain hash codes, and explores semantic similarities for enhancing the discriminant ability of hash codes. Experiments on two widely used datasets demonstrate that SPGAN can outperform state-of-the-art related works.

The heterogeneous defect prediction (HDP) technique can predict defects in a target company using heterogeneous metric data from external company, which has received substantial research attention. However, existing HDP methods assume that source data is labeled but labeling data is expensive. Semi-supervised defect prediction technique can perform defect prediction with few labeled data. In this paper, we investigate a new problem — semi-supervised HDP (SHDP). To solve this problem, we propose a new approach named cost-sensitive kernel semi-supervised correlation analysis (CKSCA) as a solution of SHDP problem. It introduces unified metric representation and canonical correlation analysis to make the data distributions of different company projects more similar. CKSCA also designs a cost-sensitive kernel semi-supervised discriminant analysis mechanism to utilize the limited labeled data and sufficient real-life unlabeled data from different companies. Besides we collect lots of open-source projects from GitHub website to construct a new large-scale unlabeled dataset called GITHUB dataset. It contains 26,407 modules and is greater than each public project dataset. It has been public online and can be extended continuously. Experiments on the GITHUB dataset and other public datasets indicate that unlabeled GITHUB data can help prediction model improve prediction performance, and CKSCA is effective and efficient for solving SHDP problem.

In this study, the authors study the problem of tensor completion, in particular for three‐dimensional arrays such as visual data. Previous works have shown that the low‐rank constraint can produce impressive performances for tensor completion. These works are often solved by means of Tucker rank. However, Tucker rank does not capture the intrinsic correlation of the tensor entries. Therefore, the authors propose a new proximal operator for the approximation of tensor nuclear norms based on tensor‐train rank‐1 decomposition via the singular value decomposition. The proximal operator will perform a soft‐thresholding operation on tensor singular values. In addition, the low‐rank constraint can capture the global structure of data well, but it does not exploit local smooth of visual data. Therefore, they integrate total variation as a regularisation term into low‐rank tensor completion. Finally, they use a primal–dual splitting to achieve optimisation. Experimental results have shown that the proposed method, can preserve the multi‐dimensional nature inherent in the data, and thus provide superior results over many state‐of‐the‐art tensor completion techniques.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

Webpage classification has attracted a lot of research interest. Webpage data is often multi-view and high-dimensional, and the webpage classification application is usually semi-supervised. Due to these characteristics, using semi-supervised multi-view feature learning (SMFL) technique to deal with the webpage classification problem has recently received much attention. However, there still exists room for improvement for this kind of feature learning technique. How to effectively utilize the correlation information among multi-view of webpage data is an important research topic. Correlation analysis on multi-view data can facilitate extraction of the complementary information. In this paper, we propose a novel SMFL approach, named semi-supervised multi-view correlation feature learning (SMCFL), for webpage classification. SMCFL seeks for a discriminant common space by learning a multi-view shared transformation in a semi-supervised manner. In the discriminant space, the correlation between intra-class samples is maximized, and the correlation between inter-class samples and the global correlation among both labeled and unlabeled samples are minimized simultaneously. We transform the matrix-variable based nonconvex objective function of SMCFL into a convex quadratic programming problem with one real variable, and can achieve a global optimal solution. Experiments on widely used datasets demonstrate the effectiveness and efficiency of the proposed approach.

Background.Solving the class-imbalance problem of within-project software defect prediction (SDP) is an important research topic. Although some class-imbalance learning methods have been presented, there exists room for improvement. For cross-project SDP, we found that the class-imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class-imbalance problem.Objective.We aim to provide effective solutions for both within-project and cross-project class-imbalance problems.Method.Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. For within-project prediction, we improve SDA for achieving balanced subclasses and propose the improved SDA (ISDA) approach. For cross-project prediction, we employ the semi-supervised transfer component analysis (SSTCA) method to make the distributions of source and target data consistent, and propose the SSTCA+ISDA prediction approach.Results. Extensive experiments on four widely used datasets indicate that: 1) ISDA-based solution performs better than other state-of-the-art methods for within-project class-imbalance problem; 2) SSTCA+ISDA proposed for cross-project class-imbalance problem significantly outperforms related methods.Conclusion. Within-project and cross-project class-imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems.

Cross-company defect prediction (CCDP) learns a prediction model by using training data from one or multiple projects of a source company and then applies the model to the target company data. Existing CCDP methods are based on the assumption that the data of source and target companies should have the same software metrics. However, for CCDP, the source and target company data is usually heterogeneous, namely the metrics used and the size of metric set are different in the data of two companies. We call CCDP in this scenario as heterogeneous CCDP (HCCDP) task. In this paper, we aim to provide an effective solution for HCCDP. We propose a unified metric representation (UMR) for the data of source and target companies. The UMR consists of three types of metrics, i.e., the common metrics of the source and target companies, source-company specific metrics and target-company specific metrics. To construct UMR for source company data, the target-company specific metrics are set as zeros, while for UMR of the target company data, the source-company specific metrics are set as zeros. Based on the unified metric representation, we for the first time introduce canonical correlation analysis (CCA), an effective transfer learning method, into CCDP to make the data distributions of source and target companies similar. Experiments on 14 public heterogeneous datasets from four companies indicate that: 1) for HCCDP with partially different metrics, our approach significantly outperforms state-of-the-art CCDP methods; 2) for HCCDP with totally different metrics, our approach obtains comparable prediction performances in contrast with within-project prediction results. The proposed approach is effective for HCCDP.