45 research outputs found
Curvature approximation from parabolic sectors
We propose an invariant three-point curvature approximation for plane curves based on the arc of a parabolic sector, and we analyze how closely this approximation is to the true curvature of the curve. We compare our results with the obtained with other invariant three-point curvature approximations. Finally, an application is discussed
Generalized Linear Models for Geometrical Current predictors. An application to predict garment fit
The aim of this paper is to model an ordinal response variable in terms
of vector-valued functional data included on a vector-valued RKHS. In particular,
we focus on the vector-valued RKHS obtained when a geometrical object (body) is
characterized by a current and on the ordinal regression model. A common way to
solve this problem in functional data analysis is to express the data in the orthonormal
basis given by decomposition of the covariance operator. But our data present very important differences with respect to the usual functional data setting. On the one
hand, they are vector-valued functions, and on the other, they are functions in an
RKHS with a previously defined norm. We propose to use three different bases: the
orthonormal basis given by the kernel that defines the RKHS, a basis obtained from
decomposition of the integral operator defined using the covariance function, and a
third basis that combines the previous two. The three approaches are compared and
applied to an interesting problem: building a model to predict the fit of children’s
garment sizes, based on a 3D database of the Spanish child population. Our proposal
has been compared with alternative methods that explore the performance of other
classifiers (Suppport Vector Machine and k-NN), and with the result of applying
the classification method proposed in this work, from different characterizations of
the objects (landmarks and multivariate anthropometric measurements instead of
currents), obtaining in all these cases worst results
A New Geometric Metric in the Shape and Size Space of Curves in R n
Shape analysis of curves in Rn is an active research topic in computer vision. While shape itself is important in many applications, there is also a need to study shape in conjunction with other features, such as scale and orientation. The combination of these features, shape, orientation and scale (size), gives different geometrical spaces. In this work, we define a new metric in the shape and size space, S2, which allows us to decompose S2 into a product space consisting of two components: S4Ă—R, where S4 is the shape space. This new metric will be associated with a distance function, which will clearly distinguish the contribution that the difference in shape and the difference in size of the elements considered makes to the distance in S2, unlike the previous proposals. The performance of this metric is checked on a simulated data set, where our proposal performs better than other alternatives and shows its advantages, such as its invariance to changes of scale. Finally, we propose a procedure to detect outlier contours in S2 considering the square-root velocity function (SRVF) representation. For the first time, this problem has been addressed with nearest-neighbor techniques. Our proposal is applied to a novel data set of foot contours. Foot outliers can help shoe designers improve their designs
Archetypal Curves in the Shape and Size Space: Discovering the Salient Features of Curved Big Data by Representative Extremes
Curves are complex data. Tools for visualizing, exploring, and discovering the structure
of a data set of curves are valuable. In this paper, we propose a scalable methodology to
solve this challenge. On the one hand, we consider two distances in the shape and size
space, one well-known distance and another recently proposed, which differentiate the
contribution in shape and in size of the elements considered to compute the distance.
On the other hand, we use archetypoid analysis (ADA) for the first time in elastic
shape analysis. ADA is a recent technique in unsupervised statistical learning, whose
objective is to find a set of archetypal observations (curves in this case), in such a way
that we can describe the data set as convex combinations of these archetypal curves.
This makes interpretation easy, even for non-experts. Archetypal curves or pure types
are extreme cases, which also facilitates human understanding. The methodology is
illustrated with a simulated data set and applied to a real problem. It is important to
know the distribution of foot shapes to design suitable footwear that accommodates
the population. For this purpose, we apply our proposed methodology to a real data
set composed of foot contours from the adult Spanish population.Funding for open access charge: CRUE-Universitat Jaume
Classification of geometrical objects by integrating currents and functional data analysis. An application to a 3D database of Spanish child population
This paper focuses on the application of Discriminant Analysis to a set of
geometrical objects (bodies) characterized by currents. A current is a relevant
mathematical object to model geometrical data, like hypersurfaces, through
integration of vector fields along them. As a consequence of the choice of a
vector-valued Reproducing Kernel Hilbert Space (RKHS) as a test space to
integrate hypersurfaces, it is possible to consider that hypersurfaces are
embedded in this Hilbert space. This embedding enables us to consider
classification algorithms of geometrical objects. A method to apply Functional
Discriminant Analysis in the obtained vector-valued RKHS is given. This method
is based on the eigenfunction decomposition of the kernel. So, the novelty of
this paper is the reformulation of a size and shape classification problem in
Functional Data Analysis terms using the theory of currents and vector-valued
RKHS. This approach is applied to a 3D database obtained from an anthropometric
survey of the Spanish child population with a potential application to online
sales of children's wear
Archetypal analysis with missing data: see all samples by looking at a few based on extreme profiles
In this paper we propose several methodologies for handling missing or incomplete data in Archetype analysis (AA) and Archetypoid analysis (ADA). AA seeks to find archetypes, which are convex combinations of data points, and to approximate the samples as mixtures of those archetypes. In ADA, the representative archetypal data belong to the sample, i.e. they are actual data points. With the proposed procedures, missing data are not discarded or previously filled by imputation and the theoretical properties regarding location of archetypes are guaranteed, unlike the previous approaches. The new procedures adapt the AA algorithm either by considering the missing values in the computation of the solution or by skipping them. In the first case, the solutions of previous approaches are modified in order to fulfill the theory and a new procedure is proposed, where the missing values are updated by the fitted values. In this second case, the procedure is based on the estimation of dissimilarities between samples and the projection of these dissimilarities in a new space, where AA or ADA is applied, and those results are used to provide a solution in the original space. A comparative analysis is carried out in a simulation study, with favorable results. The methodology is also applied to two real data sets: a well-known climate data set and a global development data set. We illustrate how these unsupervised methodologies allow complex data to be understood, even by non-experts
Archetypal shapes based on landmarks and extension to handle missing data
Archetype and archetypoid analysis are extended to shapes. The objective is to find representative shapes. Archetypal shapes are pure (extreme) shapes. We focus on the case where the shape of an object is represented by a configuration matrix of landmarks. As shape space is not a vectorial space, we work in the tangent space, the linearized space about the mean shape. Then, each observation is approximated by a convex combination of actual observations (archetypoids) or archetypes, which are a convex combination of observations in the data set. These tools can contribute to the understanding of shapes, as in the usual multivariate case, since they lie somewhere between clustering and matrix factorization methods. A new simplex visualization tool is also proposed to provide a picture of the archetypal analysis results. We also propose new algorithms for performing archetypal analysis with missing data and its extension to incomplete shapes. A well-known data set is used to illustrate the methodologies developed. The proposed methodology is applied to an apparel design problem in children
Unsupervised classification of children’s bodies using currents
Object classification according to their shape and size is of key importance in many scientific fields. This work focuses on the case where the size and shape of an object is characterized by a current. A current is a mathematical object which has been proved relevant to the modeling of geometrical data, like submanifolds, through integration of vector fields along them. As a consequence of the choice of a vector-valued reproducing kernel Hilbert space (RKHS) as a test space for integrating manifolds, it is possible to consider that shapes are embedded in this Hilbert Space. A vector-valued RKHS is a Hilbert space of vector fields; therefore, it is possible to compute a mean of shapes, or to calculate a distance between two manifolds. This embedding enables us to consider size-and-shape clustering algorithms. These algorithms are applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children’s wear