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Ravi Vaidyanathan - One of the best experts on this subject based on the ideXlab platform.

  • A Dynamic Channel Selection Strategy for Dense-Array ERP Classification
    IEEE Transactions on Biomedical Engineering, 2009
    Co-Authors: Srinivas Kota, Lalit Gupta, Dennis L. Molfese, Ravi Vaidyanathan
    Abstract:

    The goal of this paper is to introduce a new strategy to accurately classify event-related potentials (ERPs), recorded using dense electrode arrays, into predefined brain activity categories. The challenge is to exploit the enhanced spatial information offered by dense arrays while overcoming the significant increase in the Dimensionality Problem introduced by the large increase in the number of channels. These conflicting objectives are achieved by introducing a spatiotemporal-array model to observe the dense-array ERP amplitude variations across channels and time, simultaneously. To account for latency variations and EEG noise in the array elements, each spatiotemporal element in the array is initially modeled as a Gaussian random variable. A two-step process that uses the Kolmogrov-Smirnov test and the Lilliefors test is formulated to select the array elements that have different Gaussian densities across all ERP categories. Selecting spatiotemporal elements that fit the assumed model and also statistically differ across the ERP categories not only ensures high classification accuracies but also decreases the Dimensionality significantly. The selection is dynamic in the sense that selecting spatiotemporal-array elements corresponds to selecting ERP samples of different channels at different time instants. Each selected array element is classified using a univariate Gaussian classifier, and the resulting decisions are fused into a decision fusion vector that is classified using a discrete Bayes classifier. By converting an inherently multivariate classification Problem into a simpler Problem involving only univariate classifications, the Dimensionality Problem that plagues the design of practical multivariate ERP classifiers is circumvented. Consequently, classifiers can be designed to classify the ERPs that are unique to an individual without having to collect a prohibitively large ERP dataset from him/her. The application of the resulting dynamic-channel-selection-based classification strategy is demonstrated by designing and testing classifiers for eight subjects using ERPs from a Stroop color test and it is shown that the strategy yields high classification accuracies. Finally, it is noted that because of the generalized formulation of the strategy, it can be applied to various other Problems involving the classification of multivariate signals acquired from multiple identical or multiple heterogeneous sensors.

  • A DCT-Gaussian classification scheme for human-robot interface
    2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009
    Co-Authors: Srinivas Kota, Lalit Gupta, Michael Mace, Ravi Vaidyanathan
    Abstract:

    The ultimate success of a human-robot-interface system depends on how accurately user control signals are classified. This paper is aimed at developing and testing a strategy to accurately classify human-robot control signals. The primary focus is on overcoming the Dimensionality Problem frequently encountered in the design of Gaussian multivariate signal classifiers. The Dimensionality Problem is overcome by selecting, using two different ranking criteria, a small set of linear combinations of the input signal space generated by the discrete cosine transform (DCT). The application of the resulting DCT-Gaussian signal classification strategy is demonstrated by classifying tongue-movement ear-pressure (TMEP) bioacoustic signals that have been proposed for control of an assistive robotic arm. Classification results show that the DCT-Gaussian classifiers outperform classifiers described in a previous study. Most noteworthy is the fact that the Gaussian multivariate control signal classifiers developed in this paper can be designed without having to collect a prohibitively large number of training signals in order to satisfy the Dimensionality conditions. Consequently, the classification strategies will be especially beneficial for designing personalized assistive interfaces for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities.

  • Dimensionality reduction strategies for the design of human machine interface signal classifiers
    2008 IEEE International Conference on Systems Man and Cybernetics, 2008
    Co-Authors: Lalit Gupta, Srinivas Kota, Swetha Murali, Dennis Molfese, Ravi Vaidyanathan
    Abstract:

    The goal in this paper is to overcome the Dimensionality Problem related to designing human-machine-interface (HMI) signal classifiers. The dimension is decreased by selecting a small set of linear combination of the input space features using the principal components transform (PCT) and the discrete cosine transform (DCT). Issues dealing with the selection of the basis vectors of the PCT and DCT for multi-class classification Problems are addressed and four different class-dependant ranking criteria are introduced to select basis vectors from the transformed training vectors in the PCT and DCT domains. The application and evaluation of the resulting PCT and DCT based multivariate classification strategies are demonstrated by classifying ear-pressure signals and event related potentials. The signals in these experiments are typical of control signals used in HMI applications and are also typical of those in which the Dimensionality Problem occurs. Based on the evaluations and comparisons, it is concluded that the PCT and the DCT based strategies developed in this paper offer viable solutions to overcome the Dimensionality Problem that frequently plagues the design of practical HMI signal classifiers.

Jung-yi Jiang - One of the best experts on this subject based on the ideXlab platform.

  • Multilabel Text Categorization Based on Fuzzy Relevance Clustering
    IEEE Transactions on Fuzzy Systems, 2014
    Co-Authors: Jung-yi Jiang
    Abstract:

    We propose a fuzzy based method for multilabel text classification in which a document can belong to one or more than one category. In text categorization, the number of the involved features is usually huge, causing the curse of the Dimensionality Problem. Besides, a category can be a nonconvex region, which is a union of several overlapping or disjoint subregions. An automatic classification system, thus, may suffer from large memory requirements or poor performance. By incorporating fuzzy techniques, our proposed method can overcome these issues. A fuzzy relevance measure is adopted to transform high-dimensional documents to low-dimensional fuzzy relevance vectors to avoid the curse of Dimensionality Problem. A clustering technique is used to divide the relevance space into a collection of subregions which are then combined to make up individual categories. This allows complex and nonconvex regions to be created. A number of experiments are presented to show the effectiveness of the proposed method in both performance and speed.

Vladimir Trifonov - One of the best experts on this subject based on the ideXlab platform.

  • a linear solution of the four Dimensionality Problem
    EPL, 1995
    Co-Authors: Vladimir Trifonov
    Abstract:

    Modelling the measurement ("active observation") process makes it possible to express in strict terms the degree to which the logic of the observer determines what he "sees", and formalize the difficult concept of rational behaviour. Presented here is a rigorous formulation of several implicit assumptions of standard physics which leads to a first-order theory shown to possess a real-world model: if an observer's logic is Boolean, he is bound to perceive his spacetime as a four-dimensional pseudo-Riemannian manifold of signature 2, with an ideal big bang geometry. The connections between the type of an observer's logic and large-scale structure of the observable universe yield a testable prediction, existence of positive cosmological constant and suggest a non-standard integration-over-spacetime technique. They strongly favour non-local reality and deliver an operational explanation of the number of particle generations. The result casts some doubts (arising also from the necessity of renormalization procedures) that classical mathematics (i.e. the mathematics of the topos of sets) is the "natural" mathematics of our world, and offers a new candidate for this role, that differs from its classical counterpart. In general, the scheme outlines a formal way to unify the logical, physical and, possibly, psychological templates of perception, which can be briefly expressed as "physics is an exponent-image of psychology".

Saurabh Prasad - One of the best experts on this subject based on the ideXlab platform.

  • Robust spatial-spectral hyperspectral image classification for vegetation stress detection
    2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
    Co-Authors: Saurabh Prasad, Lori M. Bruce, Ramesh Shrestha
    Abstract:

    Hyperspectral imaging (HSI) techniques have been widely used for a variety of applications pertaining to vegetation species identification. With its rich spectral information, HSI is a powerful tool to detect and characterize vegetation species and their health. However, due to the high Dimensionality of HSI, a the number of training samples required to estimate the parameters of the automated target recognition (ATR) or ground-cover classification algorithms is large. To avoid this over-Dimensionality Problem, feature selection or feature extraction must be performed to reduce the Dimensionality of HSI data. This Problem is further exacerbated when spatial information is also exploited in conjunction with spectral information. In this work, we propose a feature selection approach for extracting the most meaningful spatial and spectral features for a vegetative stress detection Problem - genetic algorithms based linear discriminant analysis (GA-LDA). Experimental results show that applying GA with an appropriate fitness function in the spatial-spectral feature space is very effective at selecting the most pertinent features and yields very high classification accuracies.

  • Automated hyperspectral imagery analysis via support vector machines based multi-classifier system with non-uniform random feature selection
    2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
    Co-Authors: Sathishkumar Samiappan, Saurabh Prasad, Lori M. Bruce
    Abstract:

    Ground cover classification using remotely sensed hyperspectral data is a challenging pattern recognition Problem. The small (and expensive to collect) training sample sizes exacerbate the curse-of-Dimensionality Problem that already exists with such high dimensional feature spaces. However, Support Vector Machine (SVM) classifiers have been demonstrated to be better at handling such situations compared to other statistical classifiers. Recently, multi-classifier systems and a uniform random feature selection have proved to be very effective for hyperspectral image classification. In this paper, a support vector machines based multi-classifier system with non-uniform (spectrally-constrained) random feature selection is presented. We propose two approaches to perform such a non-uniform random-feature selection. Experimental results with the AVIRIS Indian Pines hyperspectral data demonstrate that the proposed approach outperforms regular random feature selection based on a uniform distribution.

  • Decision-Level Fusion of Spectral Reflectance and Derivative Information for Robust Hyperspectral Land Cover Classification
    IEEE Transactions on Geoscience and Remote Sensing, 2010
    Co-Authors: Hemanth Reddy Kalluri, Saurabh Prasad, Lori Mann Bruce
    Abstract:

    The developments in sensor technology have made the high-resolution hyperspectral remote sensing data available to the remote sensing analyst for ground-cover classification and target recognition tasks. The inherent high Dimensionality of such data sets and the limited ground-truth data availability in many real-life operating scenarios necessitate such hyperspectral classification systems to employ the Dimensionality reduction algorithms. Previously, it has been shown that the addition of the spectral derivatives into the feature space improves the performance of the hyperspectral image analysis systems. Although the spectral derivative features are expected to provide additional information for the classification task at hand, the conventional classification techniques are typically not suitable for such fusion since simply combining these features would result in very high dimensional feature spaces, exacerbating the over-Dimensionality Problem. In this paper, we propose an effective approach for the decision-level fusion of the spectral reflectance information with the spectral derivative information for robust land cover classification. This paper differs from previous work because we propose effective classification strategies to alleviate the increased over-Dimensionality Problem introduced by the addition of the spectral derivatives for hyperspectral classification. The studies reported in this paper are conducted within the context of both single and multiple classifier systems that are designed to handle the high-dimensional feature spaces. The experimental results are reported with handheld, airborne, and spaceborne hyperspectral data. The efficacy of the proposed approaches (using spectral derivatives and single or multiple classifiers) as quantified by the overall classification accuracy (expressed in percentage) is significantly greater than that of these systems when exploiting only the reflectance information.

Panos Trahanias - One of the best experts on this subject based on the ideXlab platform.

  • Visual tracking of independently moving body and arms
    2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009
    Co-Authors: Markos Sigalas, Haris Baltzakis, Panos Trahanias
    Abstract:

    Tracking of the upper human body is one of the most interesting and challenging research fields in computer vision and comprises an important component used in gesture recognition applications. In this paper a probabilistic approach towards arm and hand tracking is presented. We propose the use of a kinematics model together with a segmentation of the parameter space to cope with the space Dimensionality Problem. Moreover, the combination of particle filters with hidden Markov models enables the simultaneous tracking of several hypotheses for the body orientation and the configuration of each of the arms.