521,016 research outputs found
Complexity plots
In this paper, we present a novel visualization technique for assisting in observation and analysis of algorithmic\ud
complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of\ud
measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and blackbox software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application
Measuring Visual Complexity of Cluster-Based Visualizations
Handling visual complexity is a challenging problem in visualization owing to
the subjectiveness of its definition and the difficulty in devising
generalizable quantitative metrics. In this paper we address this challenge by
measuring the visual complexity of two common forms of cluster-based
visualizations: scatter plots and parallel coordinatess. We conceptualize
visual complexity as a form of visual uncertainty, which is a measure of the
degree of difficulty for humans to interpret a visual representation correctly.
We propose an algorithm for estimating visual complexity for the aforementioned
visualizations using Allen's interval algebra. We first establish a set of
primitive 2-cluster cases in scatter plots and another set for parallel
coordinatess based on symmetric isomorphism. We confirm that both are the
minimal sets and verify the correctness of their members computationally. We
score the uncertainty of each primitive case based on its topological
properties, including the existence of overlapping regions, splitting regions
and meeting points or edges. We compare a few optional scoring schemes against
a set of subjective scores by humans, and identify the one that is the most
consistent with the subjective scores. Finally, we extend the 2-cluster measure
to k-cluster measure as a general purpose estimator of visual complexity for
these two forms of cluster-based visualization
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Post-hoc model-agnostic interpretation methods such as partial dependence
plots can be employed to interpret complex machine learning models. While these
interpretation methods can be applied regardless of model complexity, they can
produce misleading and verbose results if the model is too complex, especially
w.r.t. feature interactions. To quantify the complexity of arbitrary machine
learning models, we propose model-agnostic complexity measures based on
functional decomposition: number of features used, interaction strength and
main effect complexity. We show that post-hoc interpretation of models that
minimize the three measures is more reliable and compact. Furthermore, we
demonstrate the application of these measures in a multi-objective optimization
approach which simultaneously minimizes loss and complexity
Nonlinear analysis of bivariate data with cross recurrence plots
We use the extension of the method of recurrence plots to cross recurrence
plots (CRP) which enables a nonlinear analysis of bivariate data. To quantify
CRPs, we develop further three measures of complexity mainly basing on diagonal
structures in CRPs. The CRP analysis of prototypical model systems with
nonlinear interactions demonstrates that this technique enables to find these
nonlinear interrelations from bivariate time series, whereas linear correlation
tests do not. Applying the CRP analysis to climatological data, we find a
complex relationship between rainfall and El Nino data
Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation
This article presents Individual Conditional Expectation (ICE) plots, a tool
for visualizing the model estimated by any supervised learning algorithm.
Classical partial dependence plots (PDPs) help visualize the average partial
relationship between the predicted response and one or more features. In the
presence of substantial interaction effects, the partial response relationship
can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate
the complexity of the modeled relationship. Accordingly, ICE plots refine the
partial dependence plot by graphing the functional relationship between the
predicted response and the feature for individual observations. Specifically,
ICE plots highlight the variation in the fitted values across the range of a
covariate, suggesting where and to what extent heterogeneities might exist. In
addition to providing a plotting suite for exploratory analysis, we include a
visual test for additive structure in the data generating model. Through
simulated examples and real data sets, we demonstrate how ICE plots can shed
light on estimated models in ways PDPs cannot. Procedures outlined are
available in the R package ICEbox.Comment: 22 pages, 14 figures, 2 algorithm
Evolution of complexity following a quantum quench in free field theory
Using a recent proposal of circuit complexity in quantum field theories
introduced by Jefferson and Myers, we compute the time evolution of the
complexity following a smooth mass quench characterized by a time scale in a free scalar field theory. We show that the dynamics has two distinct
phases, namely an early regime of approximately linear evolution followed by a
saturation phase characterized by oscillations around a mean value. The
behavior is similar to previous conjectures for the complexity growth in
chaotic and holographic systems, although here we have found that the
complexity may grow or decrease depending on whether the quench increases or
decreases the mass, and also that the time scale for saturation of the
complexity is of order (not parametrically larger).Comment: V2: added references, new plots, and improved discussion of results
on Section 5, V3: Few minor corrections. Published versio
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
Recommended from our members
Compositional End Members in Gale Crater, Mars
Geochemical data returned from the Mars Science Laboratory’s Curiosity rover over 1296 sols, has revealed a previously unforeseen martian geochemical complexity. Before Curiosity landed in Gale Crater, Martian SNC meteorite studies along with previous orbiter, rover and lander data showed Mars as being a predominantly basaltic planet with little magmatic differentiation. But through using ChemCam density contour plots to collate compositional data obtained by that instrument, we can identify 4 compositional end members in Gale sedimentary and igneous samples
Extended Recurrence Plot Analysis and its Application to ERP Data
We present new measures of complexity and their application to event related
potential data. The new measures base on structures of recurrence plots and
makes the identification of chaos-chaos transitions possible. The application
of these measures to data from single-trials of the Oddball experiment can
identify laminar states therein. This offers a new way of analyzing
event-related activity on a single-trial basis.Comment: 21 pages, 8 figures; article for the workshop ''Analyzing and
Modelling Event-Related Brain Potentials: Cognitive and Neural Approaches``
at November 29 - December 01, 2001 in Potsdam, German
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