211 research outputs found

    Incremental Linear Discriminant analysis for classification of Data Streams

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    This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods. © 2005 IEEE

    Median LDA : a robust feature extraction method for face recognition

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    Robust linear discriminant analysis using MOM-Qn and WMOM-Qn estimators: Coordinate-wise approach

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    Robust linear discriminant analysis (RLDA) methods are becoming the better choice for classification problems as compared to the classical linear discriminant analysis (LDA) due to their ability in circumventing outliers issue. Classical LDA relies on the usual location and scale estimators which are the sample mean and covariance matrix. The sensitivity of these estimators towards outliers will jeopardize the classification process. To alleviate the issue, robust estimators of location and covariance are proposed. Thus, in this study, two RLDA for two groups classification were modified using two highly robust location estimators namely Modified One-Step M-estimator (MOM) and Winsorized Modified One-Step M-estimator (WMOM). Integrated with a highly robust scale estimator, Qn, in the trimming criteria of MOM and WMOM, two new RLDA were developed known as RLDAMQ and RLDAWMQ respectively. In the computation of the new RLDA, the usual mean is replaced by MOM-Qn and WMOM-Qn accordingly. The performance of the new RLDA were tested on simulated as well as real data and then compared against the classical LDA. For simulated data, several variables were manipulated to create various conditions that always occur in real life. The variables were homogeneity of covariance (equal and unequal), samples (balanced and unbalanced), dimension of variables, and the percentage of contamination. In general, the results show that the performance of the new RLDA are more favorable than the classical LDA in terms of average misclassification error for contaminated data, although the new RLDA have the shortcoming of requiring more computational time. RLDAMQ works best under balanced sample sizes while RLDAWMQ surpasses the others under unbalanced sample sizes. When real financial data were considered, RLDAMQ shows capability in handling outliers with lowest misclassification error. As a conclusion, this research has achieved its primary objective which is to develop new RLDA for two groups classification of multivariate data in the presence of outliers

    Robust Face Recognition for Data Mining

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    While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

    Survey of Face Recognition using ICA

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    Nowadays, face recognition is used in number of applications as a means of authentication and verification. But the task of face recognition is not easy for a computer system. There are various ways that can be used for face recognition. This paper reviews the problem of face recognition using edge information as independent component. Here, the edge information is obtained by using LoG(Laplacian of Gaussian) and Canny edge detection methods. On the obtained information, Principle Component Analysis (PCA) is applied for preprocessing. Then, the images are trained using Independent Component Analysis (ICA). Then the images are tested
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