Latent Attributes Research Articles

Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

Factors affecting personal autonomy and perceived accessibility of people with mobility impairments in an urban transportation choice context

IntroductionThis paper studies factors affecting personal autonomy and perceived accessibility of people with mobility impairments in an urban transportation choice context, taking the city of Tunja, Colombia, as a case study. MethodsIn the context of our research, mobility impairments refer to a person's inability to use one or more of his/her extremities, or a lack of strength to walk. We conducted a survey in February 2018, using a face-to-face questionnaire consisted of three parts that were relevant to our study: facing a discrete choice experiment, rating a set of indicators of latent variables, and reporting the main socioeconomic characteristics. We estimated a hybrid discrete choice (HDC) model to identify the socioeconomic characteristics affecting personal autonomy and perceived accessibility and also test the hypothesis that these latent variables are determinants in the choice process of urban transportation mode for people with mobility impairments. ResultsThe empirical results demonstrate that perceived accessibility is different for travelers with a low income, wheelchair users, mobility impairments caused by aging and transit dependents. We also find that personal autonomy varies depending on car owners, permanent mobility impairments, wheelchair users, and workers in comparison with other people. The population heterogeneity we obtained through the latent variables allows us to identify policies to improve the quality of life for people with mobility impairments. ConclusionsThis paper advances the knowledge of how tangible and latent attributes affect the transportation choice process for people with mobility impairments by estimating an HDC model that integrates waiting time, travel time, urban transportation costs, personal autonomy, and perceived accessibility. Our results also contribute to a better understanding of the transportation needs of people with mobility impairments in developing countries.

P2V-MAP: Mapping Market Structures for Large Retail Assortments

The authors propose a new, exploratory approach for analyzing market structures that leverages two recent methodological advances in natural language processing and machine learning. They customize a neural network language model to derive latent product attributes by analyzing the co-occurrences of products in shopping baskets. Applying dimensionality reduction to the latent attributes yields a two-dimensional product map. This method is well-suited to retailers because it relies on data that are readily available from their checkout systems and facilitates their analyses of cross-category product complementarity, in addition to within-category substitution. The approach has high usability because it is automated, is scalable and does not require a priori assumptions. Its results are easy to interpret and update as new market basket data are collected. The authors validate their approach both by conducting an extensive simulation study and by comparing their results with those of state-of-the-art, econometric methods for modeling product relationships. The application of this approach using data collected at a leading German grocery retailer underlines its usefulness and provides novel findings that are relevant to assortment-related decisions.

A Partial Mastery, Higher-Order Latent Structural Model for Polytomous Attributes in Cognitive Diagnostic Assessments

The latent attribute space in cognitive diagnosis models (CDMs) is often assumed to be unstructured or saturated. In recent years, the number of latent attributes in real tests has often been found to be large, and polytomous latent attributes have been advocated. Therefore, it is preferable to adopt substantive theories to connect seemingly unrelated latent attributes, to replace the unstructured or saturated latent structural models (LSMs) with structured or parsimonious ones, with simplified parameter estimation. In the present study, we developed a partial mastery, higher-order LSM for polytomous attributes, which was built upon the framework of adjacent-category logit models to account for a higher-order latent structure of multiple polytomous attributes. The proposed model can be incorporated into many existing CDMs. We conducted simulations to evaluate the psychometric properties of the proposed model and obtained good parameter recovery. We then provided an empirical example to demonstrate the applications and the advantages of the proposed model.

Conditional Independence and Dimensionality of Cognitive Diagnostic Models: a Test for Model Fit

Nonparametric cognitive diagnosis methods are useful in cognitive diagnosis modeling for calibration efficiency, especially when sample size is small or large, or the latent attributes are more complex. This article proposes the Mantel-Haenszel chi-squared statistic as an index for detecting the misspecification of latent attributes as well as testlet effects in nonparametric cognitive diagnosis methods. The proposed theoretical considerations are augmented by simulation studies conducted to assess the performance of the Mantel-Haenszel statistic under various conditions within the nonparametric diagnosis framework, with a special focus on situations were the set of latent abilities assumed to underlie the data was underspecified.

A Longitudinal Higher-Order Diagnostic Classification Model

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach.

Deep low-rank subspace ensemble for multi-view clustering

Multi-view clustering aims to incorporate complementary information from different data views for more effective clustering. However, it is difficult to obtain the true categories of data based on complex distribution and diversified latent attributes of multi-view data. In this paper, we propose a new multi-view clustering method that integrates deep matrix factorization, low-rank subspace learning, and multiple subspace ensemble in a unified framework, which we term as the Deep Low-Rank Subspace Ensemble (DLRSE) method. DLRSE first employs deep matrix factorization to capture diverse and hierarchical structures in data for robust multi-view multi-layer data representations. Then, low-rank subspace representations are learned from the extracted factors to further reveal their correlations, from which more explicit clustering structure can be obtained. We further develop a subspace ensemble learning method with structured sparsity regularization to aggregate different subspaces into a consensus subspace, which can incorporate the intrinsic clustering structure across multiple views better. Extensive experiments on several datasets demonstrate that the proposed method can effectively explore the diversified clustering structure inherent in data and exploit multi-view complementary information, and achieve considerable improvement in performance over existing methods.

A Sequential Process Model for Cognitive Diagnostic Assessment With Repeated Attempts.

When diagnostic assessments are administered to examinees, the mastery status of each examinee on a set of specified cognitive skills or attributes can be directly evaluated using cognitive diagnosis models (CDMs). Under certain circumstances, allowing the examinees to have at least one opportunity to correctly answer the questions and assessments, with repeated attempts on the items, provides many potential benefits. A sequential process model can be extended to model repeated attempts in diagnostic assessments. Two formulations of the sequential generalized deterministic-input noisy-"and"-gate (G-DINA) model were developed in this study. The first extension uses the latent transition analysis (LTA) approach to model changes in the attributes over attempts, and the second extension constructs a higher order structure of latent continuous variables and latent attributes to account for the dependences of the attributes over attempts. Accurate model parameter estimation and correct classifications of attributes were observed in a series of simulations using Bayesian estimation. The effectiveness of the developed sequential G-DINA model was demonstrated by fitting real data from a longitudinal mathematical test to the developed model and the longitudinal G-DINA model using the LTA approach. Finally, this article closes by discussing several important issues associated with the developed models and providing suggestions for future directions.

Improving speech embedding using crossmodal transfer learning with audio-visual data

Learning a discriminative voice embedding allows speaker turns to be compared directly and efficiently, which is crucial for tasks such as diarization and verification. This paper investigates several transfer learning approaches to improve a voice embedding using knowledge transferred from a face representation. The main idea of our crossmodal approaches is to constrain the target voice embedding space to share latent attributes with the source face embedding space. The shared latent attributes can be formalized as geometric properties or distribution characterics between these embedding spaces. We propose four transfer learning approaches belonging to two categories: the first category relies on the structure of the source face embedding space to regularize at different granularities the speaker turn embedding space. The second category -a domain adaptation approach- improves the embedding space of speaker turns by applying a maximum mean discrepancy loss to minimize the disparity between the distributions of the embedded features. Experiments are conducted on TV news datasets, REPERE and ETAPE, to demonstrate our methods. Quantitative results in verification and clustering tasks show promising improvement, especially in cases where speaker turns are short or the training data size is limited. The analysis also gives insights the embedding spaces and shows their potential applications.

Causal Analysis of Software Development Attributes for Cloud Applications

Cloud computing provides organizations the dynamic capability to deploy applications quickly on self-provision development platforms. In adopting a cloud-computing paradigm, the software development process leverages the use of cloud native features. This article highlights a cloud-based health insurance solution that enables consumers to purchase packages online with the objective of identifying attributes in the context of the development process while analyzing the association among the attributes. Exploratory factor analysis is used to identify the latent attributes. Their associations, in terms of causal relationship analysis, are investigated via DEMATEL. Results of factor analysis imply that agility, availability and adaptability are the underlying factors for cloud application development. The findings of the DEMATEL reveal that co-creative and collaborative development process, which embrace quick discovery and assembly of services in the cloud, are significant attributes that influence other attributes within the context of the cloud-based software development process.

Group optimization for multi-attribute visual embedding

Understanding semantic similarity among images is the core of a wide range of computer graphics and computer vision applications. However, the visual context of images is often ambiguous as images that can be perceived with emphasis on different attributes. In this paper, we present a method for learning the semantic visual similarity among images, inferring their latent attributes and embedding them into multi-spaces corresponding to each latent attribute. We consider the multi-embedding problem as an optimization function that evaluates the embedded distances with respect to qualitative crowdsourced clusterings. The key idea of our approach is to collect and embed qualitative pairwise tuples that share the same attributes in clusters. To ensure similarity attribute sharing among multiple measures, image classification clusters are presented to, and solved by users. The collected image clusters are then converted into groups of tuples, which are fed into our group optimization algorithm that jointly infers the attribute similarity and multi-attribute embedding. Our multi-attribute embedding allows retrieving similar objects in different attribute spaces. Experimental results show that our approach outperforms state-of-the-art multi-embedding approaches on various datasets, and demonstrate the usage of the multi-attribute embedding in image retrieval application.

From signals to knowledge: A conceptual model for multimodal learning analytics

AbstractMultimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human‐driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science.

Hypothesis Testing of the Q-matrix.

The recent surge of interests in cognitive assessment has led to the development of cognitive diagnosis models. Central to many such models is a specification of the Q-matrix, which relates items to latent attributes that have natural interpretations. In practice, the Q-matrix is usually constructed subjectively by the test designers. This could lead to misspecification, which could result in lack of fit of the underlying statistical model. To test possible misspecification of the Q-matrix, traditional goodness of fit tests, such as the Chi-square test and the likelihood ratio test, may not be applied straightforwardly due to the large number of possible response patterns. To address this problem, this paper proposes a new statistical method to test the goodness fit of the Q-matrix, by constructing test statistics that measure the consistency between a provisional Q-matrix and the observed data for a general family of cognitive diagnosis models. Limiting distributions of the test statistics are derived under the null hypothesis that can be used for obtaining the test p-values. Simulation studies as well as a real data example are presented to demonstrate the usefulness of the proposed method.

Dynamic evolutionary clustering approach based on time weight and latent attributes for collaborative filtering recommendation

Collaborative filtering is one of the most widely used individual recommendation algorithms. The traditional collaborative filtering recommendation algorithm takes less care of time variation, which may be inaccurate in real surroundings. A novel dynamic evolutionary clustering algorithm based on time weight and latent attributes is proposed. According to the time effect of historical information in recommendation system, forgetting curve is introduced to better grasp the recent interest of the users. To gather users with similar interest into the same cluster, item characteristics and user attributes are mined. Therefore, network model is established by introducing the forgetting function to score matrix, utilizing item characteristics and user attributes. Items and users are regarded as heterogenous nodes in network. Furthermore, a novel dynamic evolutionary clustering algorithm is adopted to divide users and items set into K clusters, and individuals with higher similarity are clustered. The preferences of users in the same cluster are similar. Then, collaborative filtering is applied in each cluster to predict the ratings. Finally, the target users are recommended predicted according to prediction ratings. Simulations show that the presented method gains better recommendation accuracy in comparison of existing algorithms through MovieLens100k, Restaurant & consumer and CiaoDVD data sets.

Sketch-Based 3D Model Retrieval Using Attributes

With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.

Identifying Latent Structures in Restricted Latent Class Models

ABSTRACTThis article focuses on a family of restricted latent structure models with wide applications in psychological and educational assessment, where the model parameters are restricted via a latent structure matrix to reflect prespecified assumptions on the latent attributes. Such a latent matrix is often provided by experts and assumed to be correct upon construction, yet it may be subjective and misspecified. Recognizing this problem, researchers have been developing methods to estimate the matrix from data. However, the fundamental issue of the identifiability of the latent structure matrix has not been addressed until now. The first goal of this article is to establish identifiability conditions that ensure the estimability of the structure matrix. With the theoretical development, the second part of the article proposes a likelihood-based method to estimate the latent structure from the data. Simulation studies show that the proposed method outperforms the existing approaches. We further illustrate the method through a dataset in educational assessment. Supplementary materials for this article are available online.

Semi-Supervised Bayesian Attribute Learning for Person Re-Identification

Person re-identification (re-ID) tasks aim to identify the same person in multiple images captured from non-overlapping camera views. Most previous re-ID studies have attempted to solve this problem through either representation learning or metric learning, or by combining both techniques. Representation learning relies on the latent factors or attributes of the data. In most of these works, the dimensionality of the factors/attributes has to be manually determined for each new dataset. Thus, this approach is not robust. Metric learning optimizes a metric across the dataset to measure similarity according to distance. However, choosing the optimal method for computing these distances is data dependent, and learning the appropriate metric relies on a sufficient number of pair-wise labels. To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning. We introduce an Indian Buffet Process to identify the priors of the latent attributes. The dimensionality of attributes factors is then automatically determined by nonparametric Bayesian learning. Meanwhile, unlike traditional distance metric learning, we propose a re-identification probability distribution to describe how likely it is that a pair of images contains the same person. This technique relies solely on the latent attributes of both images. Moreover, pair-wise labels that are not known can be estimated from pair-wise labels that are known, making this a robust approach for semi-supervised learning. Extensive experiments demonstrate the superior performance of our algorithm over several state-of-the-art algorithms on small-scale datasets and comparable performance on large-scale re-ID datasets.

Accounting for attitudes on parking choice: An integrated choice and latent variable approach

The well-differentiated impact that parking supply options produce on congestion, pollution and land consumption arouses the interest of policy makers for a better understanding of car user’s behavior when choosing a parking option. Despite the evidence on the advantages of hybrid discrete choice models, most literature on parking choice only involves observable factors while leaving aside issues related to the latent variables. The behavioral hypothesis is that parking choice process depends not only on a set of observable factors but also has to do with individual-specific latent attributes. A hybrid discrete choice model with interactions among attitudes and observable factors, as well as among socioeconomic characteristics and observable factors, was estimated in order to consider individual heterogeneity. The results showed that, in addition to parking fee, search time and access time, a Risk-averse attitude and a Positive car care (maintenance) attitude are determinants for parking choice. The inclusion of these latent attributes and their interactions also resulted in a large improvement in the goodness-of-fit of the model and affected the time valuations.

How do we know we are measuring environmental attitude? Specific objectivity as the formal validation criterion for measures of latent attributes

In this article, our aim is to introduce the formal validation criterion of specific objectivity to environmental psychologists. Using a convenience sample of 787 respondents, we constructed a set of attitude measures. We found that all five scales represented one attribute. As expected from the Campbell Paradigm, individual attitude can be inferred from verbal acts, such as expressions of appreciation, normative expectations, intentions to engage, and self-reports of past engagement in environmentally protective behaviors. Our findings challenge (a) the common practice in psychology of defining measures by their indicators and (b) the presumption of discriminant validity between typical measures of attitudes, subjective norms, perceived behavioral control, and intentions in research within the planned behavior framework. According to our research, the same validation criterion—specific objectivity—that is applied to the measurement of latent attributes in other empirical sciences (e.g., physics) can be applied to the measurement of attitudes.

Estimating the DINA model parameters using the No-U-Turn Sampler.

The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm. The results indicated that NUTS algorithm employed in the DINA model properly recovers all parameters and is accurate for all simulated scenarios. We apply this methodology in the mental health area in order to develop a new method of classification for respondents to the Beck Depression Inventory. The implementation of this method for the DINA model applied to other psychological tests has the potential to improve the medical diagnostic process.