1,254 research outputs found
Principal manifolds and graphs in practice: from molecular biology to dynamical systems
We present several applications of non-linear data modeling, using principal
manifolds and principal graphs constructed using the metaphor of elasticity
(elastic principal graph approach). These approaches are generalizations of the
Kohonen's self-organizing maps, a class of artificial neural networks. On
several examples we show advantages of using non-linear objects for data
approximation in comparison to the linear ones. We propose four numerical
criteria for comparing linear and non-linear mappings of datasets into the
spaces of lower dimension. The examples are taken from comparative political
science, from analysis of high-throughput data in molecular biology, from
analysis of dynamical systems.Comment: 12 pages, 9 figure
Data complexity measured by principal graphs
How to measure the complexity of a finite set of vectors embedded in a
multidimensional space? This is a non-trivial question which can be approached
in many different ways. Here we suggest a set of data complexity measures using
universal approximators, principal cubic complexes. Principal cubic complexes
generalise the notion of principal manifolds for datasets with non-trivial
topologies. The type of the principal cubic complex is determined by its
dimension and a grammar of elementary graph transformations. The simplest
grammar produces principal trees.
We introduce three natural types of data complexity: 1) geometric (deviation
of the data's approximator from some "idealized" configuration, such as
deviation from harmonicity); 2) structural (how many elements of a principal
graph are needed to approximate the data), and 3) construction complexity (how
many applications of elementary graph transformations are needed to construct
the principal object starting from the simplest one).
We compute these measures for several simulated and real-life data
distributions and show them in the "accuracy-complexity" plots, helping to
optimize the accuracy/complexity ratio. We discuss various issues connected
with measuring data complexity. Software for computing data complexity measures
from principal cubic complexes is provided as well.Comment: Computers and Mathematics with Applications, in pres
Geometrical complexity of data approximators
There are many methods developed to approximate a cloud of vectors embedded
in high-dimensional space by simpler objects: starting from principal points
and linear manifolds to self-organizing maps, neural gas, elastic maps, various
types of principal curves and principal trees, and so on. For each type of
approximators the measure of the approximator complexity was developed too.
These measures are necessary to find the balance between accuracy and
complexity and to define the optimal approximations of a given type. We propose
a measure of complexity (geometrical complexity) which is applicable to
approximators of several types and which allows comparing data approximations
of different types.Comment: 10 pages, 3 figures, minor correction and extensio
Elastic principal manifolds and their practical applications
Principal manifolds serve as useful tool for many practical applications.
These manifolds are defined as lines or surfaces passing through "the middle"
of data distribution. We propose an algorithm for fast construction of grid
approximations of principal manifolds with given topology. It is based on
analogy of principal manifold and elastic membrane. The first advantage of this
method is a form of the functional to be minimized which becomes quadratic at
the step of the vertices position refinement. This makes the algorithm very
effective, especially for parallel implementations. Another advantage is that
the same algorithmic kernel is applied to construct principal manifolds of
different dimensions and topologies. We demonstrate how flexibility of the
approach allows numerous adaptive strategies like principal graph constructing,
etc. The algorithm is implemented as a C++ package elmap and as a part of
stand-alone data visualization tool VidaExpert, available on the web. We
describe the approach and provide several examples of its application with
speed performance characteristics.Comment: 26 pages, 10 figures, edited final versio
Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization
Principal manifolds are defined as lines or surfaces passing through ``the
middle'' of data distribution. Linear principal manifolds (Principal Components
Analysis) are routinely used for dimension reduction, noise filtering and data
visualization. Recently, methods for constructing non-linear principal
manifolds were proposed, including our elastic maps approach which is based on
a physical analogy with elastic membranes. We have developed a general
geometric framework for constructing ``principal objects'' of various
dimensions and topologies with the simplest quadratic form of the smoothness
penalty which allows very effective parallel implementations. Our approach is
implemented in three programming languages (C++, Java and Delphi) with two
graphical user interfaces (VidaExpert
http://bioinfo.curie.fr/projects/vidaexpert and ViMiDa
http://bioinfo-out.curie.fr/projects/vimida applications). In this paper we
overview the method of elastic maps and present in detail one of its major
applications: the visualization of microarray data in bioinformatics. We show
that the method of elastic maps outperforms linear PCA in terms of data
approximation, representation of between-point distance structure, preservation
of local point neighborhood and representing point classes in low-dimensional
spaces.Comment: 35 pages 10 figure
Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph
Large datasets represented by multidimensional data point clouds often
possess non-trivial distributions with branching trajectories and excluded
regions, with the recent single-cell transcriptomic studies of developing
embryo being notable examples. Reducing the complexity and producing compact
and interpretable representations of such data remains a challenging task. Most
of the existing computational methods are based on exploring the local data
point neighbourhood relations, a step that can perform poorly in the case of
multidimensional and noisy data. Here we present ElPiGraph, a scalable and
robust method for approximation of datasets with complex structures which does
not require computing the complete data distance matrix or the data point
neighbourhood graph. This method is able to withstand high levels of noise and
is capable of approximating complex topologies via principal graph ensembles
that can be combined into a consensus principal graph. ElPiGraph deals
efficiently with large and complex datasets in various fields from biology,
where it can be used to infer gene dynamics from single-cell RNA-Seq, to
astronomy, where it can be used to explore complex structures in the
distribution of galaxies.Comment: 32 pages, 14 figure
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