Enhanced approach for latent semantic indexing using wavelet transform

Latent semantic indexing (LSI) is a technique used for intelligent information retrieval (IR). It can be used as an alternative to traditional keyword matching IR and is attractive in this respect because of its ability to overcome problems with synonymy and polysemy. This study investigates various aspects of LSI: the effect of the Haar wavelet transform (HWT) as a preprocessing step for the singular value decomposition (SVD) in the key stage of the LSI process; and the effect of different threshold types in the HWT on the search results. The developed method allows the visualisation and processing of the term document matrix, generated in the LSI process, using HWT. The results have shown that precision can be increased by applying the HWT as a preprocessing step, with better results for hard thresholding than soft thresholding, whereas standard SVD-based LSI remains the most effective way of searching in terms of recall value.

Vulnerability and multiple deprivation perspectives on economic exclusion in Europe: A latent class analysis

In this paper we address issues relating to vulnerability to economic exclusion and levels of economic exclusion in Europe. We do so by applying latent class models to data from the European Community Household Panel for thirteen countries. This approach allows us to distinguish between vulnerability to economic exclusion and exposure to multiple deprivation at a particular point in time. The results of our analysis confirm that in every country it is possible to distinguish between a vulnerable and a non-vulnerable class. Association between income poverty, life-style deprivation and subjective economic strain is accounted for by allocating individuals to the categories of this latent variable. The size of the vulnerable class varies across countries in line with expectations derived from welfare regime theory. Between class differentiation is weakest in social democratic regimes but otherwise the pattern of differentiation is remarkably similar. The key discriminatory factor is life-style deprivation, followed by income and economic strain. Social class and employment status are powerful predictors of latent class membership in all countries but the strength of these relationships varies across welfare regimes. Individual biography and life events are also related to vulnerability to economic exclusion. However, there is no evidence that they account for any significant part of the socio-economic structuring of vulnerability and no support is found for the hypothesis that social exclusion has come to transcend class boundaries and become a matter of individual biography. However, the extent of socio-economic structuring does vary substantially across welfare regimes. Levels of economic exclusion, in the sense of current exposure to multiple deprivation, also vary systematically by welfare regime and social class. Taking both vulnerability to economic exclusion and levels of exclusion into account suggests that care should be exercised in moving from evidence on the dynamic nature of poverty and economic exclusion to arguments relating to the superiority of selective over universal social policies.

A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.

Determinants of WTP for Prosecco wine: A latent class regression with attitudinal responses

Purpose – Under investigation is Prosecco wine, a sparkling white wine from North-East Italy.
Information collection on consumer perceptions is particularly relevant when developing market
strategies for wine, especially so when local production and certification of origin play an important
role in the wine market of a given district, as in the case at hand. Investigating and characterizing the
structure of preference heterogeneity become crucial steps in every successful marketing strategy. The
purpose of this paper is to investigate the sources of systematic differences in consumer preferences.
Design/methodology/approach – The paper explores the effect of inclusion of answers to
attitudinal questions in a latent class regression model of stated willingness to pay (WTP) for this
specialty wine. These additional variables were included in the membership equations to investigate
whether they could be of help in the identification of latent classes. The individual specific WTPs from
the sampled respondents were then derived from the best fitting model and examined for consistency.
Findings – The use of answers to attitudinal question in the latent class regression model is found to
improve model fit, thereby helping in the identification of latent classes. The best performing model
obtained makes use of both attitudinal scores and socio-economic covariates identifying five latent
classes. A reasonable pattern of differences in WTP for Prosecco between CDO and TGI types were
derived from this model.
Originality/value – The approach appears informative and promising: attitudes emerge as
important ancillary indicators of taste differences for specialty wines. This might be of interest per se
and of practical use in market segmentation. If future research shows that these variables can be of use
in other contexts, it is quite possible that more attitudinal questions will be routinely incorporated in
structural latent class hedonic models.

A question of taste: Recognising the role of latent preferences and attitudes in analysing food choices

There has long been substantial interest in understanding consumer food choices, where a key complexity in this context is the potentially large amount of heterogeneity in tastes across individual consumers, as well as the role of underlying attitudes towards food and cooking. The present paper underlines that both tastes and attitudes are unobserved, and makes the case for a latent variable treatment of these components. Using empirical data collected in Northern Ireland as part of a wider study to elicit intra-household trade-offs between home-cooked meal options, we show how these latent sensitivities and attitudes drive both the choice behaviour as well as the answers to supplementary questions. We find significant heterogeneity across respondents in these underlying factors and show how incorporating them in our models leads to important insights into preferences. 

A unified view on the effect of fibers and loading on sfrc creep through linear projection to latent structures

Creep of Steel Fiber Reinforced Concrete (SFRC) under flexural loads in the cracked state and to what extent different factors determine creep behaviour are quite understudied topics within the general field of SFRC mechanical properties. A series of prismatic specimens have been produced and subjected to sustained flexural loads. The effect of a number of variables (fiber length and slenderness, fiber content, and concrete compressive strength) has been studied in a comprehensive fashion. Twelve response variables (creep parameters measured at different times) have been retained as descriptive of flexural creep behaviour. Multivariate techniques have been used: the experimental results have been projected to their latent structure by means of Principal Components Analysis (PCA), so that all the information has been reduced to a set of three latent variables. They have been related to the variables considered and statistical significance of their effects on creep behaviour has been assessed. The result is a unified view on the effects of the different variables considered upon creep behaviour: fiber content and fiber slenderness have been detected to clearly modify the effect that load ratio has on flexural creep behaviour.

Fingerprint Volatile Organic Compounds (VOC) analysis in Goat’s Milk and Halloumi Cheese as Marker for Authentication of Region of Origin

Goats’ milk is responsible for unique traditional products such as Halloumi cheese. The characteristics of Halloumi depend on the original features of the milk and on the conditions under which the milk has been produced such as feeding regime of the animals or region of production. Using a range of milk (33) and Halloumi (33) samples collected over a year from three different locations in Cyprus (A, Anogyra; K, Kofinou; P, Paphos), the potential for fingerprint VOC analysis as marker to authenticate Halloumi was investigated. This unique set up consists of an in-injector thermo desorption (VOCtrap needle) and a chromatofocusing system based on mass spectrometry (VOCscanner). The mass spectra of all the analyzed samples are treated by multivariate analysis (Principle component analysis and Discriminant functions analysis). Results showed that the highland area of product (P) is clearly identified in milks produced (discriminant score 67%). It is interesting to note that the higher similitude found on milks from regions “A” and “K” (with P being distractive; discriminant score 80%) are not ‘carried over’ on the cheeses (higher similitude between regions “A” and “P”, with “K” distinctive). Data have been broken down into three seasons. Similarly, the seasonality differences observed in different milks are not necessarily reported on the produced cheeses. This is expected due to the different VOC signatures developed in cheeses as part of the numerous biochemical changes during its elaboration compared to milk. VOC however it is an additional analytical tool that can aid in the identification of region origin in dairy products.

Applying Health Locus of Control and Latent Class Modelling to food and physical activity choices affecting CVD risk

Health Locus of Control (HLC) classifies our beliefs about the connection between our actions and health outcomes (Skinner, 1996) into three categories: “internal control”, corresponding to health being the result of an individual's effort and habits; “control by powerful others”, whereby health depends on others, such as doctors; and “chance control”, according to which health depends on fate and chance. Using Choice Experiments we investigate the relationship between HLC and willingness to change lifestyle, in terms of eating habits, physical activity and associated cardiovascular disease risk, in a 384 person sample representative of the 40–65 aged population of Northern Ireland administered between February and July 2011. Using latent class analysis we identify three discrete classes of people based on their HLC: the first class is sceptical about their capacity to control their health and certain unhealthy habits. Despite being unsatisfied with their situation, they are reluctant to accept behaviour changes. The second is a group of individuals unhappy with their current situation but willing to change through exercise and diet. Finally, a group of healthy optimists is identified, who are satisfied with their current situation but happy to take more physical activity and improve their diet. Our findings show that any policy designed to modify people's health related behaviour should consider the needs of this sceptical class which represents a considerable proportion of the population in the region.

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.