880 research outputs found
Prediction of cybersickness in virtual environments using topological data analysis and machine learning
Recent significant progress in Virtual Reality (VR) applications and environments raised several challenges. They proved to have side effects on specific users, thus reducing the usability of the VR technology in some critical domains, such as flight and car simulators. One of the common side effects is cybersickness. Some significant commonly reported symptoms are nausea, oculomotor discomfort, and disorientation. To mitigate these symptoms and consequently improve the usability of VR systems, it is necessary to predict the incidence of cybersickness. This paper proposes a machine learning approach to VR’s cybersickness prediction based on physiological and subjective data. We investigated combinations of topological data analysis with a range of classifier algorithms and assessed classification performance. The highest performance of Topological Data Analysis (TDA) based methods was achieved in combination with SVMs with Gaussian RBF kernel, indicating that Gaussian RBF kernels provide embeddings of physiological time series data into spaces that are rich enough to capture the essential geometric features of this type of data. Comparing several combinations with feature descriptors for physiological time series, the performance of the TDA + SVM combination is in the top group, statistically being on par or outperforming more complex and less interpretable methods. Our results show that heart rate does not seem to correlate with cybersickness
Topological Data Analysis in ATM: the shape of big flight data sets
Flight trajectory data sets are difficult to analyse due to several reasons,
from the high interconnectedness of all their factors to the high
dimensionality of the data. In this paper we introduce Topological Data
Analysis (TDA) and some of its techniques to extract some useful conclusions
concerning ATM data. We will show how topology encodes useful information
regarding airport analysis using the set of Spanish' airports in the Summer
Season of 2018. Finally, we present some conclusions, and some guidelines in
order to face new challenges in the ATM area.Comment: 31 page
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
Topological Data Analysis (TDA) is a recent approach to analyze data sets
from the perspective of their topological structure. Its use for time series
data has been limited. In this work, a system developed for a leading provider
of cloud computing combining both user segmentation and demand forecasting is
presented. It consists of a TDA-based clustering method for time series
inspired by a popular managerial framework for customer segmentation and
extended to the case of clusterwise regression using matrix factorization
methods to forecast demand. Increasing customer loyalty and producing accurate
forecasts remain active topics of discussion both for researchers and managers.
Using a public and a novel proprietary data set of commercial data, this
research shows that the proposed system enables analysts to both cluster their
user base and plan demand at a granular level with significantly higher
accuracy than a state of the art baseline. This work thus seeks to introduce
TDA-based clustering of time series and clusterwise regression with matrix
factorization methods as viable tools for the practitioner
Spectral Topological Data Analysis of Brain Signals
Topological data analysis (TDA) has become a powerful approach over the last
twenty years, mainly due to its ability to capture the shape and the geometry
inherent in the data. Persistence homology, which is a particular tool in TDA,
has been demonstrated to be successful in analyzing functional brain
connectivity. One limitation of standard approaches is that they use
arbitrarily chosen threshold values for analyzing connectivity matrices. To
overcome this weakness, TDA provides a filtration of the weighted brain network
across a range of threshold values. However, current analyses of the
topological structure of functional brain connectivity primarily rely on overly
simplistic connectivity measures, such as the Pearson orrelation. These
measures do not provide information about the specific oscillators that drive
dependence within the brain network. Here, we develop a frequency-specific
approach that utilizes coherence, a measure of dependence in the spectral
domain, to evaluate the functional connectivity of the brain. Our approach, the
spectral TDA (STDA), has the ability to capture more nuanced and detailed
information about the underlying brain networks. The proposed STDA method leads
to a novel topological summary, the spectral landscape, which is a
2D-generalization of the persistence landscape. Using the novel spectral
landscape, we analyze the EEG brain connectivity of patients with attention
deficit hyperactivity disorder (ADHD) and shed light on the frequency-specific
differences in the topology of brain connectivity between the controls and ADHD
patients.Comment: 28 pages, 23 figure
An industry case of large-scale demand forecasting of hierarchical components
Demand forecasting of hierarchical components is essential in manufacturing.
However, its discussion in the machine-learning literature has been limited,
and judgemental forecasts remain pervasive in the industry. Demand planners
require easy-to-understand tools capable of delivering state-of-the-art
results. This work presents an industry case of demand forecasting at one of
the largest manufacturers of electronics in the world. It seeks to support
practitioners with five contributions: (1) A benchmark of fourteen demand
forecast methods applied to a relevant data set, (2) A data transformation
technique yielding comparable results with state of the art, (3) An alternative
to ARIMA based on matrix factorization, (4) A model selection technique based
on topological data analysis for time series and (5) A novel data set.
Organizations seeking to up-skill existing personnel and increase forecast
accuracy will find value in this work
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