Search results for "curse of dimensionality"

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

2015

In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…

Spatially-induced nestedness in a neutral model of phage-bacteria networks

2017

[EN] Ecological networks, both displaying mutualistic or antagonistic interactions, seem to share common structural traits: the presence of nestedness and modularity. A variety of model approaches and hypothesis have been formulated concerning the significance and implications of these properties. In phage-bacteria bipartite infection networks, nestedness seems to be the rule in many different contexts. Modeling the coevolution of a diverse virus¿host ensemble is a difficult task, given the dimensionality and multi parametric nature of a standard continuous approximation. Here, we take a different approach, by using a neutral, toy model of host¿phage interactions on a spatial lattice. Each …

2021

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and w…

Partitioned learning of deep Boltzmann machines for SNP data.

2016

Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…

Assessing Procrastination

2019

Abstract. The short form of the General Procrastination Scale (GPS-K; Klingsieck & Fries, 2012 ; Lay, 1986 ) is a reliable self-report scale measuring general procrastination. The presumed one-dimensional factor structure of the scale, however, has never been examined. Thus, the purposes of this representative study were to examine its dimensionality and factorial invariance across age and sex, and to provide norm values of the German general population. The GPS-K was administered to a representative community sample ( N = 2,527; age range 14–95 years). A confirmatory factor analysis (CFA) was conducted. To explore convergent validity, standardized scales of distress and life satisfact…

The dimensionality of human's electroencephalogram during sleep.

1991

In order to perform an analysis of nonlinear EEG-dynamics we investigated the EEG of ten male probands during sleep. According to Rechtschaffen and Kales (1968) we scored the sleep-EEG and applied an algorithm, proposed by Grassberger and Proccaccia (1983) to compute the correlation dimension of different sleep stages. The correlation dimension characterizes the dynamics of the EEG signal and estimates the degrees of freedom of the signal under study. We could demonstrate, that the EEG of slow wave sleep stages depicts a dimensionality, which is two units smaller than that of light or REM sleep.

A Nonlinear Approach to Brain Function: Deterministic Chaos and Sleep EEG

1992

In order to perform a nonlinear dimensional analysis of the sleep electroencephalogram (EEG), we applied an algorithm proposed by Grassberger and Procaccia to calculate the correlation dimension D2 of different sleep stages under Lorazepam medication versus placebo. This correlation dimension characterizes the dynamics of the sleep EEG and it estimates the degrees of freedom of the signal under study. We demonstrate that slow-wave sleep depicts a much smaller dimensionality than light or rapid eye movement (REM) sleep, and that Lorazepam does not alter the EEG's dimensionality except in stage II and REM.

Changes in power curve shapes as an indicator of fatigue during dynamic contractions.

2010

The purpose of this study was to analyze exercise-induced leg fatigue during a dynamic fatiguing task by examining the shapes of power vs. time curves through the combined use of several statistical methods: B-spline smoothing, functional principal components and (supervised and unsupervised) classification. In addition, granulometric size distributions were also computed to allow for comparison of curves coming from different subjects. Twelve physically active men participated in one acute heavy-resistance exercise protocol which consisted of five sets of 10 repetition maximum leg press with 120 s of rest between sets. To obtain a smooth and accurate representation of the data, a basis of …

Self-Other Differentiation Scale: Dimensionality, IRT Parameterization, and Measurement Invariance

2018

The Self-Other Differentiation Scale (Olver, Aries, & Batgos, 1989) is a self-report instrument assessing the experience of a separate sense of self from others. The authors aimed to examine its dimensionality, reliability, and measurement invariance across gender. It was completed by 348 participants (48% men) from 17 to 30 years old in Study 1, 348 participants (40% men) from 18 to 28 years old in Study 2, and 1,068 participants (49% men) from 17 to 28 years old in Study 3. The results supported the hypothesis of just one factor underlying the scale; they also showed an appropriate internal consistency and a partial measurement invariance across gender. Results also showed evidence fo…

Earth system data cubes unravel global multivariate dynamics

2020

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cu…