Engineering topics

Lee, Chulhee

Publications and source records attributed to Lee, Chulhee.

9 records

NASA NTRS1993

Analyzing High-Dimensional Multispectral Data

In this paper, through a series of specific examples, we illustrate some characteristics encountered in analyzing high- dimensional multispectral data. The increased importance of the second-order statistics in analyzing high-dimensional data is illustrated, as is the shortcoming of classifiers such as the minimum distance classifier which rely on first-order variations alone. We also illustrate how inaccurate estimation or first- and second-order statistics, e.g., from use of training sets which are too small, affects the performance of a classifier. Recognizing the importance of second-order statistics on the one hand, but the increased difficulty in perceiving and comprehending information present in statistics derived from high-dimensional data on the other, we propose a method to aid visualization of high-dimensional statistics using a color coding scheme.

Lee, Chulhee↗

NASA NTRS1993

Decision Boundary Feature Extraction for Nonparametric Classification

Feature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining 'discriminantly informative features' and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for non-parametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

Lee, Chulhee↗

NASA NTRS1993

Feature Extraction Based on Decision Boundaries

In this paper, a novel approach to feature extraction for classification is proposed based directly on the decision boundaries. We note that feature extraction is equivalent to retaining informative features or eliminating redundant features; thus, the terms 'discriminantly information feature' and 'discriminantly redundant feature' are first defined relative to feature extraction for classification. Next, it is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A novel characteristic of the proposed method arises by noting that usually only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is therefore introduced. Next, a procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) It predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms.

Lee, Chulhee↗

NASA NTRS1993

Feature extraction and classification algorithms for high dimensional data

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Lee, Chulhee↗

NASA NTRS1992

Decision boundary feature extraction for neural networks

We propose a new feature extraction method for neural networks. The method is based on the recently published decision boundary feature extraction algorithm. It has been shown that all the necessary features for classification can be extracted from the decision boundary. To apply the decision boundary feature extraction method, we first define the decision boundary in neural networks. Next, we propose a procedure for extracting all the necessary features for classification from the decision boundary. The proposed algorithm preserves the characteristics of neural networks, which can define arbitrary decision boundary. Experiments show promising results.

Lee, Chulhee↗

NASA NTRS1992

Analyzing high dimensional data

Problems encountered in analyzing high dimensional data are discussed and possible solutions are proposed. The increased importance of second-order statistics in analyzing high dimensional data and the shortcoming of the minimum distance classifier in high dimensional data are recognized. By investigating characteristics of high dimensional data, it is shown that second-order statistics must be taken into account in high dimensional data. There is a need to represent second order statistics effectively. As the data dimensionality increases, it becomes more difficult to perceive and compare information present in statistics derived from data. In order to overcome this problem, a method to visualize statistics using color code is proposed. By representing statistics using a color code, the first and the second statistics can be more readily compared.

Lee, Chulhee↗

NASA NTRS1992

Feature selection for neural networks using Parzen density estimator

A feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected using the decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, an attempt is made to reduce the training time of the neural network and obtain a simpler neural network, which further reduces the classification time for test data.

Lee, Chulhee↗

NASA NTRS1991

Fast likelihood classification

A multistage classification that reduces the processing time substantially is proposed. This classification algorithm consists of several stages, and in each stage likelihood values of classes are calculated and compared. If a class has a likelihood value less than a threshold, the class if truncated at that stage as an unlikely class, thus reducing the number of classes for which likelihood values are to be calculated at the next stage. Thus a host of classes can be truncated using a small portion of the total features at early stages, resulting in substantial reduction of computing time. Several truncation criteria are developed, and the relationship between thresholds and the error caused by the truncation is investigated. Experiments show that the proposed algorithm reduces the processing time by the factor of 3-7, depending on the number of classes and features, while maintaining essentially the same accuracies.

Lee, Chulhee↗

NASA NTRS1991

Decision boundary feature selection for non-parametric classifier

Feature selection has been one of the most important topics in pattern recognition. Although many authors have studied feature selection for parametric classifiers, few algorithms are available for feature selection for nonparametric classifiers. In this paper we propose a new feature selection algorithm based on decision boundaries for nonparametric classifiers. We first note that feature selection for pattern recognition is equivalent to retaining 'discriminantly informative features', and a discriminantly informative feature is related to the decision boundary. A procedure to extract discriminantly informative features based on a decision boundary for nonparametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

Lee, Chulhee↗