Fast Multi-View Semi-Supervised Learning With Learned Graph

In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is learned by exploiting the consensus of distinct views in the entire tracking. Besides, semi-supervised learning alleviates the lack of sufficient labeled samples in the tracking task, resulting in significant improvement in generalization performance. By showing that the sample data is block-circulant, we diagonalize it with the Discrete Fourier Transform to keep the tracking at high speed. Using features extracted by the VGG-19 network and in a 1:1 ratio of the labeled samples to the unlabeled, the experiment results on the CVPR2013 Online Object Tracking Benchmark show the effectiveness of our multi-view semi-supervised tracking model.

Contributions from the physical laboratory of the university of Pennsylvania. No. 1.—A new vertical-lantern galvanometer

Multi-view multi-label learning has become a research focus for describing objects with rich expressions and annotations. However, real-world data often contains numerous unlabeled instances, due to the high cost and technical limitations of manual labeling. This crucial problem involves three main challenges: i) How to extract advanced semantics from available views? ii) How to build a refined classification framework with limited labeled space? iii) How to provide more high-quality supervisory information? To address these problems, we propose a Semi-Supervised Multi-View Multi-Label Learning Method with View-Specific Transformer and Enhanced Pseudo-Label named SMVTEP. Specifically, Generative Adversarial Networks are employed to extract informative shared and specific representations and their consistency and distinctiveness are ensured through the adversarial mechanism and information theory based contrastive learning. Then we build specific classifiers for each extracted feature and apply instance-level manifold constraints to reduce bias across classifiers. Moreover, we design a transformer-style fusion approach that simultaneously captures the imbalance of expressive power among views, mapping effects on specific labels, and label dependencies by incorporating confidence scores and category semantics into the self-attention mechanism. Furthermore, after using Mixup for data augmentation, category-enhanced pseudo-labels are leveraged to improve the reliability of additional annotations by aligning the label distribution of unlabeled samples with the true distribution. Finally, extensive experimental results validate the effectiveness of SMVTEP against state-of-the-art methods.

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

Flexible multi-view semi-supervised learning with unified graph

Advances in single-cell biotechnologies have generated the single-cell RNA sequencing (scRNA-seq) of gene expression profiles at cell levels, providing an opportunity to study cellular distribution. Although significant efforts developed in their analysis, many problems remain in studying cell types distribution because of the heterogeneity, high dimensionality, and noise of scRNA-seq. In this study, a multi-view clustering with graph learning algorithm (MCGL) for scRNA-seq data is proposed, which consists of multi-view learning, graph learning, and cell type clustering. In order to avoid a single feature space of scRNA-seq being inadequate to comprehensively characterize the functions of cells, MCGL constructs the multiple feature spaces and utilizes multi-view learning to comprehensively characterize scRNA-seq data from different perspectives. MCGL adaptively learns the similarity graphs of cells that overcome the dependence on fixed similarity, transforming scRNA-seq analysis into the analysis of multi-view clustering. MCGL decomposes the networks of cells into view-specific and common networks in multi-view learning, which better characterizes the topological relationship of cells. MCGL simultaneously utilizes multiple types of cell-cell networks and fully exploits the connection relationship between cells through the complementarity between networks to improve clustering performance. The graph learning, graph factorization, and cell-type clustering processes are accomplished simultaneously under one optimization framework. The performance of the MCGL algorithm is validated with ten scRNA-seq datasets from different scales, and experimental results imply that the proposed algorithm significantly outperforms fourteen state-of-the-art scRNA-seq algorithms.

Simple SummaryTracking the movements of chickens is important for understanding their well-being. Traditional methods for measuring chicken mobility are time-consuming and cannot provide real-time information. In this study, we, the researchers, used a combination of artificial intelligence methods and computer algorithms to track individual chickens. Using these methods, it was possible to detect and track individual chickens in two pens. We analyzed the data to see how far and how fast each chicken moved every hour and every day. Compared to manual measurements, the combined model provided more accurate measurements of the mobility of each chicken and the entire group, even when some chickens were hidden, or the detection was not perfect. This study may serve to effectively track indicators critical for broiler production performance and welfare.Mobility is a vital welfare indicator that may influence broilers’ daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model’s mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers’ hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.

Multi-view subspace learning via bidirectional sparsity

Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech labeling is both expensive and time-consuming while there is a huge amount of unlabeled data easily available nowadays, semi-supervised learning (SSL) from both labeled and unlabeled data which aims to reduce the development cost for LVCSR becomes more important than ever. In this paper, we propose SSL for LVCSR by using the multiple views learned from different acoustic features and randomized decision trees. In addition, we develop the multi-objective learning of HMM-based acoustic models by optimizing a hybrid criterion which is established by the combination of the discriminative mutual information from labeled data and the entropy from unlabeled data. Experiments conducted on Broadcast News show the benefits of proposed methods.

As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets.

Multi-view learning machines aim to process multi-view data set which consists of instances with different views. In recent years, the scale of multi-view data set is more and more larger and traditional multi-view learning machines which should store the entire data set cannot process them with the limitation of computation and storage ability. In order to overcome such a disadvantage, one-pass multi-view (OPMV) framework has been proposed and it goes through instances only once without storing the entire data set. But most multi-view data sets are semi-supervised, i.e., some training instances of the data sets are labeled while others are unlabeled. Furthermore, for a real-world multi-view data set, labeling instances is a high-cost task. In this paper, we propose semi-supervised OPMV (SSOPMV) framework for solving this issue. In SSOPMV, we develop an approach to generate additional unlabeled instances which possess useful discriminant information and apply them into the original OPMV framework along with the labeled and original unlabeled training instances. We verify the effectiveness of the SSOPMV with some real-world semi-supervised multi-view data sets used theoretically and empirically.

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.

Ground penetrating radar (GPR) can be used for efficient and non-destructive detection of internal diseases in asphalt pavement, allowing for fast acquisition of a large amount of GPR image data. Currently, the identification of pavement diseases mainly depends on manual work, which significantly affects the efficiency of disease identification, and the identification results are prone to be affected by the operator’s personal opinions. This paper presents a method of identifying hidden diseases in asphalt pavement based on semi-supervised deep learning. Based on the YOLOv5 network, supervised learning was conducted on a limited number of existing samples to obtain the initial network model for disease identification. Then, the samples not subjected to manual labeling were identified and labeled, thus ensuring a balance in the types and the quantity of disease samples. Finally, a relatively comprehensive dataset was used for training to improve the identification precision of the intelligent model. The experimental results show that the overall precision of disease identification can reach 92% through the semi-supervised learning mode and the deep learning with expanded training dataset. The precision of identification results obtained from the semi-supervised learning model was improved by 24% compared to that obtained from the supervised learning model. The results also demonstrate that the new intelligent approach proposed in this paper can more effectively tap into the potential learning abilities of the model, thereby improving the precision of subgrade disease identification, and thus achieving accurate and efficient identification of pavement diseases.

This paper proposes two new semi-supervised clustering methods based on the combination of multiview,active and semi-supervised learning. Farthest-first traversal scheme is proposed to select the seed set for each cluster. Under the multi-view framework,these two proposed algorithms explore the active learning from two aspects, that is, active seed set selection and active query construction. Experimental results on both Chinese and English data sets show that our proposed algorithms outperform the baseline Constrained KMeans(CKM) and its active version(ACKM).

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.