Search results for "curse of dimensionality"

Mixed Valence Materials: Prussian Blue Analogues of Reduced Dimensionality (Small 16/2012)

2012

Enthalpic and entropic contributions of water molecules to the functional T → R transition of human hemoglobin in solution

1992

Generalized solvent-mediated forces contribute to free energy at the functional T → R transition of human hemoglobin A (HbA). Their contribution is here sorted out quantitatively in both its enthalpic and entropic parts, along with the average number of water molecules involved. The latter (about 75 waters in average) must be considered together with HbA as one statistically defined functional unit for oxygen transport. Their configurations are expected to undergo frequent structural rearrangements. Lifetimes of statistically relevant configurations do not need to (although, of course, they may) exceed by more than a factor 5 the normal H-bond lifetimes of the pure solvent. Compared to the …

Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine

2013

Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial ne…

Regularized RBF Networks for Hyperspectral Data Classification

2004

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.

Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy

2018

AIF acknowledge support by the US Department of Energy, Office of Basic Energy Sciences under Grant No. DE-FG02 03ER15476. AIF acknowledges support by the Laboratory Directed Research and Development Program through LDRD 18-047 of Brookhaven National Laboratory under U.S. Department of Energy Contract No. DE-SC0012704 for initiating his research in machine learning methods. The help of the beamline staff at ELETTRA (project 20160412) synchrotron radiation facility is acknowledged. RMC-EXAFS and MD-EXAFS simulations were performed on the LASC cluster-type computer at Institute of Solid State Physics of the University of Latvia.

Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors.

2013

Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single…

A local complexity based combination method for decision forests trained with high-dimensional data

2012

Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how thi…

Dimensionality reduction via regression on hyperspectral infrared sounding data

2014

This paper introduces a new method for dimensionality reduction via regression (DRR). The method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. These properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular…

A Feature Set Decomposition Method for the Construction of Multi-classifier Systems Trained with High-Dimensional Data

2013

Data mining for the discovery of novel, useful patterns, encounters obstacles when dealing with high-dimensional datasets, which have been documented as the "curse" of dimensionality. A strategy to deal with this issue is the decomposition of the input feature set to build a multi-classifier system. Standalone decomposition methods are rare and generally based on random selection. We propose a decomposition method which uses information theory tools to arrange input features into uncorrelated and relevant subsets. Experimental results show how this approach significantly outperforms three baseline decomposition methods, in terms of classification accuracy.

Incremental Generalized Discriminative Common Vectors for Image Classification.

2015

Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without th…