Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces (2008)
Shalom Darmanjian, Jose C. Principe
We propose two algorithms that decompose the joint likelihood of observing multidimensional neural input data into marginal likelihoods. The first algorithm, boosted mixtures of hidden Markov chains...
Anant Hegde, Deniz Erdogmus, Deng S. Shiau, Jose C. Principe, Chris J. Sackellares
Abnormal dynamical coupling between brain structures is believed to be primarily responsible for the generation of epileptic seizures and their propagation. In this study, we attempt to identify the...
Clustering using Renyi's Entropy (2003)
Robert Jenssen, Deniz Erdogmus, Jose C. Principe, Torbjrn Eltoft
We propose a new clustering algorithm using Renyi's entropy as our similarity metric. The main idea is to assign a data pattern to the cluster, which among all possible clusters, increases its...
Design and Implementation of Biologically Realistic Signal to Symbol Translators (2003)
Principe, Jose C., Tavares, Vitor
This paper reviews the problem of translating signals into symbols preserving maximally the information contained in the signal time structure. In this context we motivate the use of nonconvergent...
Frank M. Candocia, Jose C. Principe
We present and illustrate the use of a bottleneck system for the segmentation and super-resolution of ISAR targets. The system is shown to be comprised of three basic subsystems: a compressing...
Superresolution Of Images Based On Local Correlations (2002)
Frank M. Candocia, Jose C. Principe
An adaptive two step paradigm for the superresolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image...
Proceedings of SPIE: Algorithms for Synthetic Aperture Radar Imagery V, April 1998. (2002)
This paper introduces a methodology for the superresolution of synthetic aperture radar (SAR) images using multiple target and clutter models. The system has two major components: a mechanism that...
Superresolution Of Images With (2002)
Frank M. Candocia, Jose C. Principe
This chapter develops a superresolution methodology and specifies an architecture to magnify and make clear small-sized and/or low-fidelity images. Superresolution is particularly relevant for...
Comments on 'Sinc Interpolation of Discrete Periodic Signals' (2002)
Frank C, Student Member, Jose C. Principe, Senior Member
Recently, the convolution of the sinc kernel with the infinite sequence of a periodic function was expressed as a finite summation. The expression obtained, however, is not numerically stable when...
A Neural Implementation of Interpolation with a Family of Kernels (2002)
Frank M. Candocia, Jose C. Principe
A paradigm for interpolating images based on a family of kernels is presented. Each kernel is "tuned" to specific image characteristics and contains the information responsible for the local creation...
Optimization In Companion Search Spaces: The Case Of Crossentropy (2000)
Craig L. Fancourt, Jose C. Principe
We present a new learning algorithm for the supervised training of multilayer perceptrons for classification that is significantly faster than any previously known method. Like existing methods, the...
Statistically Independent Feature Extraction for SAR Imagery (2000)
This paper reports on the work conducted in the University of Florida Computational NeuroEngineering Laboratory during 1998 under a DARPA grant. We have developed and applied a new feature extraction...
Pose Estimation in SAR using an Information Theoretic Criterion (2000)
This paper describes a pose estimation algorithm based on an information theoretic formulation. We formulate the pose estimation statistically and show that pose can be estimated from a low...
Qun Zhao, Victor Brennan, Dongxin Xu, Zheng Wang, Jose C. Principe, Ph. D
This paper describes a new architecture for synthetic aperture radar (SAR) automatic target recognition (ATR) based on the premise that the pose of the target is estimated within a high degree of...
A New Clustering Evaluation Function Using Renyi's Information Potential (2000)
Erhan Gokcay, Jose C. Principe
Clustering is an important unsupervised learning paradigm, but so far the traditional methodologies are mostly based on the minimization of the variance between the data and the cluster means. Here...
Learning From Examples with Quadratic Mutual Information (2000)
this paper, a Quadratic Mutual Information measure for a set of discrete samples is introduced and a brief description of the learning algorithm is presented.
Craig L. Fancourt, Jose C. Principe
With the advent of efficient algorithms and fast computers for training neural networks, it is now feasible to employ neural network predictors in the generalized likelihood ratio (GLR) test for the...
Deniz Erdogmus, Jose C. Principe
The error-entropy-minimization approach in adaptive system training is investigated. The effect of Parzen windowing on the location of the global minimum of entropy has been investigated. An...
Learning from Examples with Quadratic Mutual Information (2000)
This paper discusses a novel algorithm to train nonlinear mappers with information theoretic criteria (entropy or mutual information) directly from a training set. The method is based on a Parzen...
Information-Theoretic Learning Using Renyi's Quadratic Entropy (2000)
Learning from examples has been traditionally based on correlation or on the mean square error (MSE) criterion, in spite of the fact that learning is intrinsically related with the extraction of...
Jose C. Principe, Ludong Wang, Mark A. Motter
The technique of local linear models is appealing for modeling complex time series due to the weak assumptions required and its intrinsic simplicity. Here, instead of deriving the local models from...
Super-Resolution Of Images Based On Local Correlations (2000)
Frank M. Candocia, Jose C. Principe
An adaptive two step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image...
An Interactive Learning Environment for Adaptive Systems Instruction (2000)
Jose C. Principe, Neil Euliano, Curt Lefebvre
We have developed a new computer based learning environment to teach adaptive systems in the EE undergraduate curriculum. The learning environment is based upon an electronic book containing a...
Training MLPs Layer-by-Layer with the Information Potential (2000)
In the area of information processing one fundamental issue is how to measure the statistical relationship between two variables based only on their samples. In a previous paper, the idea of...
Support Vector Machines For Synthetic Aperture Radar Automatic Target Recognition (2000)
: Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real...
Information-Theoretic Learning (2000)
This chapter seeks to extend the ubiquitous mean-square error criterion (MSE) to cost functions that include more information about the training data. Since the learning process ultimately should...
Innovating Adaptive and Neural Systems Instruction with Interactive Electronic Books (2000)
Jose C. Principe, Neil R. Euliano, W. Curt Lefebvre
This paper describes an integrated strategy to innovate teaching in the undergraduate classroom by appropriately utilizing information technologies. The innovation is an interactive learning...
Generalized Eigen-Decomposition with an On-line Local Algorithm (2000)
Dongxin Xu, Jose C. Principe, Hsiao-chun Wu
This letter presents a novel, on-line, local learning algorithm to obtain generalized eigenvalues and their corresponding eigenvectors in descending order with a linear adaptive filter. The filter is...
Target Prescreening based on a Quadratic Gamma Discriminator (2000)
Jose C. Principe, Alex Radisavljevic, John Fisher Iii, Margarita Hiett, Leslie M. Novak
This paper presents the development, analysis and validation of a new target discrimination module for synthetic aperture radar (SAR) imagery based on an extension of gamma functions to 2-D. Using...
Dynamic Subgrouping in RTRL Provides a Faster O(N²) Algorithm (2000)
Neil R. Euliano, Jose C. Principe
Static grouping of processing elements (PEs) has been proposed to reduce the computational complexity of RTRL from O(n 4 ) to O(n 2 ), but performance suffers. This paper proposes a dynamic...
Craig L. Fancourt, Jose C. Principe
We find a close relationship between the discrete KarhunenLoeve transform (KLT) and the discrete prolate spheroidal wave functions (DPSWF). We show that the DPSWF form a natural basis for an...
Teaching Adaptive Systems with an Interactive, Electronic Book (2000)
Jose C. Principe, Neil R. Euliano, W. Curt Lefebvre
This paper summarizes our efforts to develop a new computer based learning environment to teach adaptive systems in the EE undergraduate curriculum. The learning environment is based upon an...
A Novel Measure for Independent Component Analysis (ICA) (2000)
Dongxin Xu, Jose C. Principe, John Fisher Iii, Hsiao-chun Wu
Measures of independence (and dependence) are fundamental in many areas of engineering and signal processing. Shannon introduced the idea of Information Entropy which has a sound theoretical...
A Spatio-Temporal Memory Based on SOMs with Activity Diffusion (2000)
Neil R. Euliano, Jose C. Principe, Neurodimension Inc
This paper discusses the use of the biologically inspired concept of activity diffusion to create a spatio-temporal memory in the SOM and neural gas algorithms. The activity diffusion creates a...
Hsiao-chun Wu, Jose C. Principe
Various existing criteria to characterize the statistical independence are applied in blind source separation and independent component analysis. However, almost all of them are based on parametric...
A Self-organizing Principle for Segmenting and Super-resolving ISAR Images (2000)
Frank M. Candocia, Jose C. Principe
We present and illustrate the use of a bottleneck system for the segmentation and super-resolution of ISAR targets. The system is shown to be comprised of three basic subsystems: a compressing...
A New Clustering Evaluation Function Using Renyi's Information Potential (2000)
Erhan Gokcay, Jose C. Principe
Clustering is an important unsupervised learning paradigm, but so far the traditional methodologies are mostly based on the minimization of the variance between the data and the cluster means. Here...
Information-Theoretic Learning Using Renyi's Quadratic Entropy (2000)
Learning from examples has been traditionally based on correlation or on the mean square error (MSE) criterion, in spite of the fact that learning is intrinsically related with the extraction of...
Supervised Learning without Numerical Targets - An Information Theoretic Approach (2000)
Ernst Haselsteiner, Jose C. Principe
This paper describes a new approach in supervised learning for classification problems. Usually each example is assigned a label, which represents the class information of the example. During...
From Hyperplanes to Large Margin Classifiers: Applications to SAR ATR (2000)
Qun Zhao, Jose C. Principe, Dongxin Xu
In this paper, the structural risk minimization (SRM) criterion is employed to train a large margin classifier, the support vector machine (SVM). Its relative performance is compared with traditional...
Craig L. Fancourt, Jose C. Principe
With the advent of efficient algorithms and fast computers for training neural networks, it is now feasible to employ neural network predictors in the generalized likelihood ratio (GLR) test for the...
Craig L. Fancourt, Jose C. Principe
We find a close relationship between the discrete KarhunenLoeve transform (KLT) and the discrete prolate spheroidal wave functions (DPSWF). We show that the DPSWF form a natural basis for an...
Pose Estimation for SAR Automatic Target Recognition (1999)
Qun Zhao, Dongxin Xu, Jose C. Principe
1 This paper explores statistically pose estimation in SAR ATR. Based on our proposed method of maximizing mutual information, further experiments are conducted by using the MSTAR/IU Database....
Spatiotemporal Dynamics of Human Epileptic Seizures (1998)
Leonidas D. Iasemidis, Jose C. Principe, J. Chris Sackellares, Neurology Service
neral population. These disturbances (seizures) result in a variety of intermittent clinical phenomena including motor, sensory, affective, cognitive, autonomic and psychic symptomology. In human...
John W. Fisher III and Jose C. Principe (1998)
The minimum average correlation energy (MACE) filter, which is linear and shift invariant, has been used extensively in the area of automatic target detection and recognition (ATD/R). We present a...
Experimental results using a nonlinear extension of the MACE filter (1998)
The minimum average correlation energy filter (MACE) filter has been shown to have superior performance for rejecting out of class inputs in pattern recognition applications. The MACE filter exhibits...
Feature Extraction Using an Information Theoretic Framework (1998)
This report addresses the rejection of confusers, the last piece of the work conducted under the contract F33615-97-1-1019. The performance of the information theoretic feature extraction is...
State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced...
Acquisition and Recognition of Moving Targets and Enabling Technologies. Volume 1 (1998)
State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced...
Recent Advances to Nonlinear MACE Filters (1998)
We present recent advances in the development of nonlinear extensions to the minimum average correlation energy (MACE) filter. The MACE filter and its variations have been applied to the area of...
A Gamma Memory Neural Network For System Identification (1998)
Mark A. Motter, Jose C. Principe
A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control...
A Method Using Multiple Models To Superresolve SAR Imagery (1998)
This paper introduces a methodology for the superresolution of synthetic aperture radar (SAR) images using multiple target and clutter models. The system has two major components: a mechanism that...
Temporal Plasticity in Self-Organizing Networks (1998)
Neil R. Euliano, Jose C. Principe
We propose a new principle that adds temporal plasticity to self-organizing networks. The algorithm uses activity diffusion to couple space and time into a single set of dynamics that can help...
A Self-Organizing Temporal Pattern Recognizer with Application to Robot Landmark Recognition (1998)
Neil R. Euliano, Jose C. Principe, Pedro Kulzer
. We propose a new principle that self-organizes an array of processing elements to recognize sequences of input events. The system maps the input onto an output space where the processing elements...
Modeling Time Dependencies in the Mixture of Experts (1998)
Craig L. Fancourt, Jose C. Principe
The Mixture of Experts, as it was originally formulated, is a static algorithm in the sense that the output of the network, and parameter updates during training, are completely independent from one...
A Neural Implementation of Interpolation with a Family of Kernels (1998)
Frank M. Candocia, Jose C. Principe
: A paradigm for interpolating images based on a family of kernels is presented. Each kernel is "tuned" to specific image characteristics and contains the information responsible for the local...
Magnitude Spectral Estimation Via Poisson Moments With Application To Speech Recognition (1998)
We propose to use the Gamma filter as a continuous time spectral feature extractor for the preprocessing of speech signals. The Gamma filter is a simple analog structure which can be implemented as a...
Learning to Generate a Sinewave with a Recurrent TwoNeuron Network (1998)
Jyh-ming Kuo, Jose C. Principe
this paper, we study an autonomous two-processing-element (PE) network and propose a method to decompose the task of determining the network parameters. The theoretical analysis and experimental...
On-Line Stochastic Functional Smoothing Optimization for Neural Network Training (1998)
Chuan Wang, Jose C. Principe, C. Principe, Ph. D
: A set of new algorithms based on an on-line implementation of a well known global optimization strategy based on stochastic functional smoothing are proposed for training neural networks. These...
Detection of Random Vectors Using an Unsupervised Neural Network (1998)
Chuan Wang, Li-kang Yen, Jose C. Principe
: In this paper, we consider the detection of a random vector in the presence of additive noise. First, we point out the relationship between linear optimal quadratic detector and the principal...
The Gamma Model- A New Neural Model for Temporal Processing (1998)
Bert De Vries, Jose C. Principe
In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns...
On-Line Transform Domain LMS Algorithm Implemented with PCA Learning (1998)
Chuan Wang, L-k Yen, Jose C. Principe
An on-line transform domain Least Mean Square (LMS) algorithm based on a neural approach is proposed. A temporal Principal Component Analysis (PCA) network is used as an orthonormalization layer in...
Reconstructed Dynamics and Chaotic Signal Modeling (1998)
Jyh-ming Kuo, Jose C. Principe
: A nonlinear AR model is derived from the reconstructed dynamics of a signal. The underlying system is assumed to be nonlinear, autonomous, and determinstic. In this formulation, the output error...
Unsupervised Learning for Nonlinear Synthetic Discriminant Functions (1998)
It has been shown in previous work 5,12 that the family of filters which includes the minimum average correlation energy (MACE) filter 7 can be formulated as a linear associative memory (LAM) 3...
Dynamic Modelling of Chaotic Time Series with Neural Networks (1998)
Jose C. Principe, Jyh-ming Kuo
This paper discusses the use of artificial neural networks for dynamic modelling of time series. We argue that multistep prediction is more appropriate to capture the dynamics of the underlying...
A Markov Framework for the Simple Genetic Algorithm (1998)
Principe Page, Thomas E. Davis, Jose C. Principe
This paper develops a theoretical framework based on Markov chains for the simple genetic algorithm (operators of reproduction, crossover, and mutation). We prove the existence of a unique stationary...
Hsiao-chun Wu, Jose C. Principe
Blind source separation and blind output decorrelation are two well-known problems in signal processing. For instantaneous mixtures, blind source separation is equivalent to a generalized...
Experimental results using a nonlinear extension of the MACE filter (1998)
The minimum average correlation energy filter (MACE) filter has been shown to have superior performance for rejecting out of class inputs in pattern recognition applications. The MACE filter exhibits...
Spectral Feature Extraction Using Poisson Moments (1998)
. We propose to use the Gamma filter [1] as a feature extractor for the preprocessing of speech signals. Gamma filter which can be implemented as a cascade of identical first order lowpass filters...
Analysis Of Spectral Feature Extraction Using The Gamma Filter (1998)
We study the Focused Gamma Network and its special case TDNN for speech recognition from a signal representation point of view and show that these structures extract the features of the signals input...
Recent Advances to Nonlinear MACE Filters (1998)
We present recent advances in the development of nonlinear extensions to the minimum average correlation energy (MACE) filter. The MACE filter and its variations have been applied to the area of...
Prediction of Chaotic Time Series Using Recurrent Neural Networks (1998)
Jyh-ming Kuo, Jose C. Principe, Bert Devries
In this paper, we propose to train and use a recurrent artificial neural network(ANN) to predict a chaotic time series. Instead of predicting the next sample in the time series as is normally done,...
Analog VLSI Implementations of Continuous-Time Memory Structures (1998)
Jui-kuo Juan, John G. Harris, Jose C. Principe
The continuous-time implementation of the popular transversal #lter is problematic since it is impossible to implement an ideal time delay in continuoustime hardware. We believe that building an...
Training Neural Networks With Additive Noise in The Desired Signal (1998)
: A new global optimization strategy for training adaptive systems such as neural networks and adaptive filters (finite or infinite impulse response (FIR or IIR)) is proposed in this paper. Instead...
Generalized Feedforward Structures: A New Class of Adaptive Filters (1998)
Jose C. Principe, Bert De Vries
In this paper we introduce a new class of filters, the generalized feedforward structures, that combine attractive properties of the moving average (MA) filters for adaptation (i.e. fast algorithms,...
Parametric Least Squares Approximation Using Gamma Bases (1998)
We study the problem of linear approximation of a signal using the parametric Gamma bases in L 2 space. These bases have a time scale parameter which has the effect of modifying the relative angle...
Backpropagation Through Time with Fixed Memory Size Requirements (1998)
U. Of Florida, Jose C. Principe, Jyh-ming Kuo
this memory (memory traces) are convolutions of the input with E w ij
THE GAMMA FILTER - A New Class of Adaptive IIR Filters with Restricted Feedback (1998)
Jose C. Principe, Bert De Vries
In this paper we introduce the generalized feedforward filter, a new class of adaptive filters that combine attractive properties of Finite Impulse Response (FIR) filters with some of the power of...
Craig L. Fancourt, Jose C. Principe
A new off-line technique for the competitive identification of piecewise stationary time series is described. A neighborhood map of one step predictors competes for the data during training. The...
Noise reduction in state space using the Focused Gamma Neural Network (1998)
Jose C. Principe, Jyh-ming Kuo
In this paper we utilize the gamma neural model to improve the signal to noise ratio (SNR) of broadband signals corrupted by white noise. The projection of a noisy signal onto the signal subspace can...
A Nonlinear Extension of the MACE Filter (1998)
The minimum average correlation energy (MACE) filter, which is linear and shift invariant, has been used extensively in the area of automatic target detection and recognition (ATD/R). We present a...
Modeling Applications with the Focused Gamma Net (1998)
Jose C. Principe, Bert De Vries, Jyh-ming Kuo
The focused gamma network is proposed as one of the possible implementations of the gamma neural model. The focused gamma network is compared with the focused backpropagation network and TDNN for a...
Jose C. Principe, Chuan Wang, Hsiao-chun Wu
: This paper proposes a network architecture to compute on-line the temporal crosscorrelation function between two signals, either stationary or locally stationary. We show that the weights of a...
Prediction of Chaotic Time Series with Neural Networks (1998)
Jose C. Principe, Alok Rathie, Jyh-ming Kuo
This paper shows that the dynamics of nonlinear systems that produce complex time series can be captured in a model system. The model system is an artificial neural network, trained with...
A Cost Function for Robust Estimation of PCA (1998)
Chuan Wang, Hsiao-chun Wu, Jose C. Principe
: It is well known that Principal Components Analysis (PCA) is optimal in the sense of Mean Square Error (MSE). However, the estimation based on MSE is sensitive to noise or outliers, therefore, it...
Spatio-Temporal Self-Organizing Feature Maps (1998)
Neil R. Euliano, Jose C. Principe
Thus far, the success of capturing and classifying temporal information with neural networks has been limited. Our methodology adds a spatio-temporal coupling to the Self-Organized Feature Map (SOFM)...
Analysis of Short Term Memories for Neural Networks (1998)
Jose C. Principe, Hui-h. Hsu, Jyh-ming Kuo
Short term memory is indispensable for the processing of time varying information with artificial neural networks. In this paper a model for linear memories is presented, and ways to include memories...