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.
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually ...low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD 2 L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD 2 L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD 2 L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD 2 L (MVSLD 2 L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.
Heterogeneous defect prediction (HDP) refers to predicting defect-proneness of software modules in a target project using heterogeneous metric data from other projects. Existing HDP methods mainly ...focus on predicting target instances with single source. In practice, there exist plenty of external projects. Multiple sources can generally provide more information than a single project. Therefore, it is meaningful to investigate whether the HDP performance can be improved by employing multiple sources. However, a precondition of conducting HDP is that the external sources are available. Due to privacy concerns, most companies are not willing to share their data. To facilitate data sharing, it is essential to study how to protect the privacy of data owners before they release their data. In this paper, we study the above two issues in HDP. Specifically, to utilize multiple sources effectively, we propose a multi-source selection based manifold discriminant alignment (MSMDA) approach. To protect the privacy of data owners, a sparse representation based double obfuscation algorithm is designed and applied to HDP. Through a case study of 28 projects, our results show that MSMDA can achieve better performance than a range of baseline methods. The improvement is 3.4-15.3 percent in g-measure and 3.0-19.1 percent in AUG.
Image annotation has attracted a lot of research interest, and multi-label learning is an effective technique for image annotation. How to effectively exploit the underlying correlation among labels ...is a crucial task for multi-label learning. Most existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between the label and the features of images untouched. Although, recently some methods attempt toward exploiting the label correlation in the input feature space by using the label information, they cannot effectively conduct the learning process in both the spaces simultaneously, and there still exists much room for improvement. In this paper, we propose a novel multi-label learning approach, named multi-label dictionary learning (MLDL) with label consistency regularization and partial-identical label embedding MLDL, which conducts MLDL and partial-identical label embedding simultaneously. In the input feature space, we incorporate the dictionary learning technique into multi-label learning and design the label consistency regularization term to learn the better representation of features. In the output label space, we design the partial-identical label embedding, in which the samples with exactly same label set can cluster together, and the samples with partial-identical label sets can collaboratively represent each other. Experimental results on the three widely used image datasets, including Corel 5K, IAPR TC12, and ESP Game, demonstrate the effectiveness of the proposed approach.
Software defect prediction has always been a hot research topic in the field of software engineering owing to its capability of allocating limited resources reasonably. Compared with cross-project ...defect prediction (CPDP), heterogeneous defect prediction (HDP) further relaxes the limitation of defect data used for prediction, permitting different metric sets to be contained in the source and target projects. However, there is still a lack of a holistic understanding of existing HDP studies due to different evaluation strategies and experimental settings. In this paper, we provide an empirical study on HDP approaches. We review the research status systematically and compare the HDP approaches proposed from 2014 to June 2018. Furthermore, we also investigate the feasibility of HDP approaches in CPDP. Through extensive experiments on 30 projects from five datasets, we have the following findings: (1) metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability. Overall, the HDP approach proposed by Li et al. (CTKCCA) currently has the best performance. (2) Handling class imbalance problems can boost the prediction effects, but the improvements are usually limited. In addition, utilizing mixed project data cannot improve the performance of HDP approaches consistently since the label information in the target project is not used effectively. (3) HDP approaches are feasible for cross-project defect prediction in which the source and target projects have the same metric set.
Software defect prediction is one of the most popular research topics in software engineering. It aims to predict defect-prone software modules before defects are discovered, therefore it can be used ...to better prioritise software quality assurance effort. In recent years, especially for recent 3 years, many new defect prediction studies have been proposed. The goal of this study is to comprehensively review, analyse and discuss the state-of-the-art of defect prediction. The authors survey almost 70 representative defect prediction papers in recent years (January 2014–April 2017), most of which are published in the prominent software engineering journals and top conferences. The selected defect prediction papers are summarised to four aspects: machine learning-based prediction algorithms, manipulating the data, effort-aware prediction and empirical studies. The research community is still facing a number of challenges for building methods and many research opportunities exist. The identified challenges can give some practical guidelines for both software engineering researchers and practitioners in future software defect prediction.
Video-based person re-identification (re-id) is an important application in practice. Since large variations exist between different pedestrian videos, as well as within each video, it is challenging ...to conduct re-id between the pedestrian videos. In this paper, we propose a simultaneous intra-video and inter-video distance learning (SI 2 DL) approach for the video-based person re-id. Specifically, SI 2 DL simultaneously learns an intra-video distance metric and an inter-video distance metric from the training videos. The intra-video distance metric is used to make each video more compact, and the inter-video one is used to ensure that the distance between truly matching videos is smaller than that between wrong matching videos. Considering that the goal of distance learning is to make truly matching video pairs from different persons be well separated with each other, we also propose a pair separation-based SI 2 DL (P-SI 2 DL). P-SI 2 DL aims to learn a pair of distance metrics, under which any two truly matching video pairs can be well separated. Experiments on four public pedestrian image sequence data sets show that our approaches achieve the state-of-the-art performance.
Recently, a multi-view dictionary learning (DL) technique has received much attention. Although some multi-view DL methods have been presented, they suffer from the problem of performance ...degeneration when large noise exists in multiple views. In this paper, we propose a novel multi-view DL approach named multi-view low-rank DL (MLDL) for image classification. Specifically, inspired by the low-rank matrix recovery theory, we provide a multi-view dictionary low-rank regularization term to solve the noise problem. We further design a structural incoherence constraint for multi-view DL, such that redundancy among dictionaries of different views can be reduced. In addition, to enhance efficiency of the classification procedure, we design a classification scheme for MLDL, which is based on the idea of collaborative representation based classification. We apply MLDL for face recognition, object classification and digit classification tasks. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.
•We offer a multi-view low-rank dictionary learning method for image classification.•Multi-view dictionary low-rank regularization term is designed to handle noise.•Structural incoherence constraint is given to reduce redundancy in dictionaries.•Multi-view collaborative representation based classification scheme is provided.•Effectiveness and efficiency of our method are demonstrated on four datasets.
Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a semi-supervised multi-view deep discriminant representation ...learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network. Unlike existing shared and specific multi-view representation learning methods that ignore the redundancy problem in representation learning, SMDDRL incorporates the orthogonality and adversarial similarity constraints to reduce the redundancy of learned representations. Moreover, to exploit the information contained in unlabeled data, we design a semi-supervised learning framework by combining deep metric learning and density clustering. Experimental results on three typical multi-view learning tasks, i.e., webpage classification, image classification, and document classification demonstrate the effectiveness of the proposed approach.
Current unsupervised feature selection methods cannot well select the effective features from the corrupted data. To this end, we propose a robust unsupervised feature selection method under the ...robust principal component analysis (PCA) reconstruction criterion, which is named the adaptive weighted sparse PCA (AW-SPCA). In the proposed method, both the regularization term and the reconstruction error term are constrained by the <inline-formula> <tex-math notation="LaTeX">\ell _{2,1} </tex-math></inline-formula>-norm: the <inline-formula> <tex-math notation="LaTeX">\ell _{2,1} </tex-math></inline-formula>-norm regularization term plays a role in the feature selection, while the <inline-formula> <tex-math notation="LaTeX">\ell _{2,1} </tex-math></inline-formula>-norm reconstruction error term plays a role in the robust reconstruction. The proposed method is in a convex formulation, and the selected features by it can be used for robust reconstruction and clustering. Experimental results demonstrate that the proposed method can obtain better reconstruction and clustering performance, especially for the corrupted data.
