Latent Semantic Topics Research Articles

Trends in major intensive care medicine journals: A machine learning approach

PurposeIntensive care medicine (ICM) has the particularity of being a multidisciplinary specialty and its literature reflects this multidisciplinarity. However, the proportion of each field in this literature and its trend dynamics are not known. The objective of this study was to analyze the ICM literature, extract latent topics and search for the presence of research trends. Material and methodsAbstracts of original articles from the top ICM journals, from their inception until December 31st, 2019, were included. This corpus was fed into a structural topic modeling algorithm to extract latent semantic topics. The temporal distribution was then analyzed and the presence of trends was searched by Mann-Kendall trends tests. ResultsFinally, 49,276 articles from 10 journals were included. After topic modeling analysis and experts' feedback, 124 research topics were selected and labeled. Topics were categorized into 19 categories, the most represented being respiratory, fundamental and neurological research. Increasing trends were observed for research on mechanical ventilation and decreasing trends for cardiopulmonary resuscitation. ConclusionsThis study reviewed all articles from major ICM journals in a comprehensive way. It provides a better understanding of ICM research landscape by analyzing the temporal evolution of latent research topics in the ICM literature.

Structural Topic Modeling: A Machine Learning Model to Text Analyzing Corporate Diversity Statement

Although text data are ubiquitous in organizations, the advancement of text analysis methods has created an unfortunate bottleneck for many organizational researchers. This paper introduced the Structure Text Model (STM; Roberts et al., 2014), a cutting-edge text modeling method that uses an unsupervised machine learning model to statistically derive latent semantic topics underlying a collection of text documents. More importantly, the STM method has the advantage of modeling document-level variables (i.e., metadata) as covariates, which is critical for organizational research. We also demonstrated the application of STM in diversity research: comprehensively analyzing a large number of diversity statements publicly released by Fortune 1000 companies. Our structural topic modeling uncovered six underlying latent semantic topics: 1) general DEI terms, 2) supporting Black community, 3) acknowledging Black community, 4) committing to diversifying workforce, 5) miscellaneous words, and 6) titles and companies. We further explored and found that the prevalence of these topics varied as a function of company characteristics, including industry sector, CEO race, corporate political orientation, etc. Our paper not only demonstrates the promising application of Structural Text Models in organizational research, but also provides important theoretical implications for current diversity research through the meaningful findings.

Research on Intelligent Evaluation System of Sports Training based on Video Image Acquisition and Scene Semantics

This paper proposes to check the travel target of the dynamic background in the video surveillance with a fixed camera. A travel target detection method based on video picture acquisition and scene semantics for surveillance video was proposed. First, on the basis of combing the concepts and methods of picture recognition, the semantic information of the scene was fused to eliminate the interference factors in the unnecessary detection area. Secondly, a remote sensing picture visual feature representation method containing a semantic recognition method of remote sensing picture scenes and CSIFT features based on PLSA was presented. 10 types of typical remote sensing picture scenes are used for tests, and the visual vocabulary extraction method remains the same. The fixed visual vocabulary was 600, and the potential semantic subjects changes between 8∼50. The test results indicated that the highest average recognition rate was obtained when the latent semantic topics were 20. Inappropriate latent semantic topics will lead to a decline in recognition rates. The effectiveness of this method was fully verified.

Improving personalised query reformulation with embeddings

As a mechanism to guide users towards a better representation of their information needs, the query reformulation method generates new queries based on users’ historical queries. To preserve the original search intent, query reformulations should be context-aware and should attempt to meet users’ personal information needs. The mainstream method aims to generate candidate queries first, according to their past frequencies, and then score (re-rank) these candidates based on the semantic consistency of terms, dependency among latent semantic topics and user preferences. We exploit embeddings (i.e. term, user and topic embeddings) to use contextual information and individual preferences more effectively to improve personalised query reformulation. Our work involves two major tasks. In the first task, candidate queries are generated from an original query by substituting or adding one term, and the contextual similarities between the terms are calculated based on the term embeddings and augmented with user personalisation. In the second task, the candidate queries generated in the first task are evaluated and scored (re-ranked) according to the consistency of the semantic meaning of the candidate query and the user preferences based on a graphical model with the term, user and topic embeddings. Experiments show that our proposed model yields significant improvements compared with the current state-of-the-art methods.

Epidemic Intelligence for the Crowd, by the Crowd

Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Each patch in SAR images has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this article proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then, the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. Finally, each image patch is quantified by discriminative latent semantic topics instead of low-level representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.

Discriminative Joint-Feature Topic Model With Dual Constraints for WCE Classification.

Wireless capsule endoscopy (WCE) enables clinicians to examine the digestive tract without any surgical operations, at the cost of a large amount of images to be analyzed. The main challenge for automatic computer-aided diagnosis arises from the difficulty of robust characterization of these images. To tackle this problem, a novel discriminative joint-feature topic model (DJTM) with dual constraints is proposed to classify multiple abnormalities in WCE images. We first propose a joint-feature probabilistic latent semantic analysis (PLSA) model, where color and texture descriptors extracted from same image patches are jointly modeled with their conditional distributions. Then the proposed dual constraints: visual words importance and local image manifold are embedded into the joint-feature PLSA model simultaneously to obtain discriminative latent semantic topics. The visual word importance is proposed in our DJTM to guarantee that visual words with similar importance come from close latent topics while the local image manifold constraint enforces that images within the same category share similar latent topics. Finally, each image is characterized by distribution of latent semantic topics instead of low level features. Our proposed DJTM showed an excellent overall recognition accuracy 90.78%. Comprehensive comparison results demonstrate that our method outperforms existing multiple abnormalities classification methods for WCE images.

Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging.

Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.

Bag of Visual Words Method based on PLSA and Chi-Square Model for Object Category

The problem of visual words’ synonymy and ambiguity always exist in the conventional bag of visual words (BoVW) model based object category methods. Besides, the noisy visual words, so-called “visual stop-words” will degrade the semantic resolution of visual dictionary. In view of this, a novel bag of visual words method based on PLSA and chi-square model for object category is proposed. Firstly, Probabilistic Latent Semantic Analysis (PLSA) is used to analyze the semantic co-occurrence probability of visual words, infer the latent semantic topics in images, and get the latent topic distributions induced by the words. Secondly, the KL divergence is adopt to measure the semantic distance between visual words, which can get semantically related homoionym. Then, adaptive soft-assignment strategy is combined to realize the soft mapping between SIFT features and some homoionym. Finally, the chi-square model is introduced to eliminate the “visual stop-words” and reconstruct the visual vocabulary histograms. Moreover, SVM (Support Vector Machine) is applied to accomplish object classification. Experimental results indicated that the synonymy and ambiguity problems of visual words can be overcome effectively. The distinguish ability of visual semantic resolution as well as the object classification performance are substantially boosted compared with the traditional methods.

Multilabel image annotation based on double-layer PLSA model.

Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset.

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the `tracking error accumulation problem'. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel `twin-pipeline' training framework to find the latent semantic topics of `moving people', the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.

Automatic Image Annotation by Fusing Semantic Topics

Automatic image annotation has become an important issue, due to the existence of a semantic gap. Based on probabilistic latent semantic analysis (PLSA), this paper presents an approach to annotate and retrieve images by fusing semantic topics. First, in order to precisely model training data, each image is represented as a bag of visual words. Then, a probabilistic model is designed to capture latent semantic topics from visual and textual modalities, respectively. Furthermore, an adaptive asymmetric learning approach is proposed to fuse these semantic topics. For each image document, the topic distribution of each modality is fused by multiplying different weights, which is determined by the entropy of the distribution of visual words. Consequently, the probabilistic model can predict semantic annotations for an unseen image because it associates visual and textual modalities properly. This approach is compared with several other state-of-the-art approaches on a standard Corel dataset. The experimental results show that this approach performs more effectively and accurately.

Harmonium Models for Video Classification

AbstractAccurate and efficient video classification demands the fusion of multimodal information and the use of intermediate representations. Combining the two ideas into one framework, we propose a series of probabilistic models for video representation and classification using intermediate semantic representations derived from multimodal features of video. On the basis of a class of bipartite undirected graphical models named harmonium, we propose dual‐wing harmonium (DWH) model that represents video shots as latent semantic topics derived by jointly modeling the transcript keywords and color‐histogram features of the data. Our family‐of‐harmonium (FoH) and hierarchical harmonium (HH) model extends DWH by introducing variables representing category labels of data, which allows data representation and classification to be performed in the same model. Our models are among the few attempts of using undirected graphical models for representing and classifying video data. Experiments on a benchmark video collection show different semantic interpretations of video data under our models, as well as superior classification performance in comparison with several directed models. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000‐000, 2008