196,149 research outputs found
Model-based Methods of Classification: Using the mclust Software in Chemometrics
Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance.
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
Model-based Methods of Classification: Using the mclust Software in Chemometrics
Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
On morphological hierarchical representations for image processing and spatial data clustering
Hierarchical data representations in the context of classi cation and data
clustering were put forward during the fties. Recently, hierarchical image
representations have gained renewed interest for segmentation purposes. In this
paper, we briefly survey fundamental results on hierarchical clustering and
then detail recent paradigms developed for the hierarchical representation of
images in the framework of mathematical morphology: constrained connectivity
and ultrametric watersheds. Constrained connectivity can be viewed as a way to
constrain an initial hierarchy in such a way that a set of desired constraints
are satis ed. The framework of ultrametric watersheds provides a generic scheme
for computing any hierarchical connected clustering, in particular when such a
hierarchy is constrained. The suitability of this framework for solving
practical problems is illustrated with applications in remote sensing
The Evolution of Shopping Center Research: A Review and Analysis
Retail research has evolved over the past sixty years. Christaller\u27s early work on central place theory, with its simplistic combination of range and threshold has been advanced to include complex consumer shopping patterns and retailer behavior in agglomerated retail centers. Hotelling\u27s seminal research on competition in a spatial duopoly has been realized in the form of comparison shopping in regional shopping centers. The research that has followed Christaller and Hoteling has been as wide as it has been deep, including literature in geography, economics, finance, marketing, and real estate.
In combination, the many extensions of central place theory and retail agglomeration economics have clearly enhanced the understanding of both retailer and consumer behavior. In addition to these two broad areas of shopping center research, two more narrowly focused areas of research have emerged. The most recent focus in the literature has been on the positive effects large anchor tenants have on smaller non-anchor tenant sales. These positive effects are referred to as retail demand externalities. Exploring the theoretical basis for the valuation of shopping centers has been another area of interest to researchers. The primary focus of this literature is based in the valuation of current and expected lease contracts
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