Search results for "Dimension"
showing 10 items of 2766 documents
Cluster formation and rheology of photoreactive nanoparticle dispersions.
2008
We show how photocrosslinking of small nanoparticles within a very dilute colloidal dispersion leads to the formation of large fractal particle clusters, which have a strong influence on the viscosity of the dispersion although the overall solid content is well below 5 wt %. Furthermore, the solvent plays an important role because of its function as an optical filter, for example, in toluene only photocrosslinking but no photocleavage takes place. Therefore, a diffusion-controlled cluster growth mechanism, leading to clusters with low fractal dimension, is expected; on the other hand, in tetrahydrofuran the photoreaction is partially reversible. Therefore, the cluster growth in this case is…
Nonlinear data description with Principal Polynomial Analysis
2012
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA. Unlike recently proposed nonlinear methods (e.g. spectral/kernel methods and projection pursuit techniques, neural networks), PPA features are easily interpretable and the method leads to a fully invertible transform, which is a desirable property…
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap
2015
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…
AN AGENT BASED ARCHITECTURE FOR MANUFACTURING E-MARKETPLACES
2005
This chapter presents the Agent Based Architecture developed within the research project, titled "Process and Production Planning in manufacturing Enterprise Networks". As mentioned in Chapter 1, the architecture has been developed to support “added value services” in neutral linear e-marketplaces, i.e. in virtual districts. In this chapter the architecture will be described from a functional and dynamic point of view by using the formalisms used in the project. In particular, from a functional perspective, the architecture is described by using the IDEF0 formalism, while its dynamics are specified by UML activity diagrams.
Local dimensionality reduction within natural clusters for medical data analysis
2005
Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before applying a learning algorithm. Especially it is important for multidimensional heterogeneous data, presented by a large number of features of different types. Dimensionality reduction is one commonly applied approach. The goal of this paper is to study the impact of natural clustering on dimensionality reduction for classification. We compare several data mining strategies that apply dimensionality reduction by means of feature extraction or feature selection for subsequent classification. We show experimentally on micr…
Three-dimensional object detection under arbitrary lighting conditions
2006
A novel method of 3D object recognition independent of lighting conditions is presented. The recognition model is based on a vector space representation using an orthonormal basis generated by the Lambertian reflectance functions obtained with distant light sources. Changing the lighting conditions corresponds to multiplying the elementary images by a constant factor and because of that, all possible lighting views will be elements that belong to that vector space. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated 3D object and that subspace. We define the angle in terms of linear correlations to get shift and i…
Analysis of the Effect of Human Presence on a Wireless Sensor Network
2011
Wireless Sensor Networks (WSNs) are gaining an increasing industry wide adoption. However, there remain major challenges such as network dimensioning and node placement especially in Built Environment Networks (BENs). Decisions on the node placement, orientation, and the number of nodes to cover the area of interest are usually ad-hoc. Ray tracing tools are traditionally employed to predict RF signal propagation; however, such tools are primarily intended for outdoor environments. RF signal propagation varies greatly indoors due to building materials and infrastructure, obstacles, node placement, antenna orientation and human presence. Because of the complexity of signal prediction, these f…
A novel method for network intrusion detection based on nonlinear SNE and SVM
2017
In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…
Semisupervised kernel orthonormalized partial least squares
2012
This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…
Adaptation of a German Multidimensional Networking Scale into English
2011
Networking refers to building and maintaining personal contacts in order to obtain resources that, in turn, enhance one’s career success and work performance. This study reports the translation and adaptation of a multifaceted German networking scale ( Wolff & Moser, 2006 ) into English and focuses on the equivalence of the two language versions. Going beyond the often used translation-backtranslation method, we used a parallel translation-backtranslation method in combination with two expert committees to arrive at the English scale version, aiming to obtain at least structural equivalence. We utilize a bilingual sample (N = 76) as well as monolingual samples from the US (N = 174) and…