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51 results for “High–Dimensional Data”
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Research ArticleOpen accessPhrase in the titleA Test on the Multivariate Behrens–Fisher Problem in High–Dimensional Data by Block Covariance EstimationParanut Sukcharoen, Samruam ChongcharoenIn this paper, we proposed a new testing statistic for testing the equality of mean vectors from two multivariate normal populations when the covariance matrices are unknown and unequal in high–dimensional data. A new test is proposed based on the idea of keeping more information from the sample covariance matrices as much as possible. A proposed test is invariant under … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 2 Apr 2019 · 7,369 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleCOMBINATION OF DIFFERENCE SUBSPACE AND OPPURTUNISTIC CLUSTERING ON HIGH DIMENSIONAL DATAM. Ravichandran, A. ShanmugamClustering is recognized as sigificant technique for analysing data and concentric effort has been taken in different domains comprises of recognition of pattern, statistical analysis and data mining for decades. Subspace clustering is developed from the group of cluster objects from all subspaces of a dataset. During clustering of objects involing higher dimension, the accuracy and effectiveness of traditional clustering … …Journal of Computer Science, Vol. 10, Issue 8 · 11 Mar 2014 · 6,355 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Test for Two-Sample Repeated Measures Designs: Effect of High-Dimensional DataBoonyarit Choopradit, Samruam ChongcharoenProblem statement: High-dimensional repeated measures data are increasingly encountered in various areas of modern science since classical multivariate statistics, e.g,. Hotelling’s T2, are not well defined in the case of high-dimensional data. Approach: In this study, the test statistics with no specific form of covariance matrix for analyzing high-dimensional two-sample repeated measures designs with common equal covariance are proposed. The … …Journal of Mathematics and Statistics, Vol. 7, Issue 4 · 28 Oct 2011 · 7,328 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFeature Selection for High Dimensional Data: An Evolutionary Filter ApproachAnwar Ali Yahya, Addin Osman, Abd Rahman Ramli, Adlan BalolaProblem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself directly to feature selection; … …Journal of Computer Science, Vol. 7, Issue 5 · 9 May 2011 · 7,946 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleShots Temporal Prediction Rules for High-Dimensional Data of Semantic Video RetrievalShaimaa Toriah Mohamed Toriah, Atef Zaki Ghalwash, Aliaa A.A. YoussifTemporal consistency stands as a vital property in semantic video retrieval. Few research studies can exploit this useful property. Most of the used methods in those studies depend on rules defined by experts and use ground-truth annotation. The Ground-truth annotation is time-consuming, labor intensive and domain specific. Additionally, it involves a limited number of annotated concepts and a limited number … …American Journal of Applied Sciences, Vol. 15, Issue 1 · 30 Jan 2018 · 8,117 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleOne-Sided Multivariate Tests for High Dimensional DataSamruam ChongcharoenProblem statement: For a multivariate normal population with size smaller than dimension, n<p, the likelihood ratio tests of the null hypothesis that the mean vector was zero with a one-sided alternative were no longer valid because they involved with sample covariance matrix which was singular. Approach: The test statistics for one-sided multivariate hypotheses with n<p were proposed. Results: The simulation … …Journal of Mathematics and Statistics, Vol. 8, Issue 2 · 20 Jun 2012 · 7,261 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Class of Region-preserving Space Transformations for Indexing High-dimensional DataRatko Orlandic, Jack LukaszukThis study introduces a class of region preserving space transformation (RPST) schemes for accessing high-dimensional data. The access methods in this class differ with respect to their space-partitioning strategies. The study develops two new static partitioning schemes that can split each dimension of the space within linear space complexity. They also support an effective mechanism for handling skewed data in … …Journal of Computer Science, Vol. 1, Issue 1 · 31 Mar 2005 · 6,919 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPerformance Analysis of Classification Algorithms on Medical Diagnoses-a SurveyS. Vanaja, K. RameshkumarThe aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and … …Journal of Computer Science, Vol. 11, Issue 1 · 22 Sep 2014 · 7,359 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Feature Selection for Elderly Health Condition ClassificationRattanawadee PanthongFeature selection is a pivotal technique for constructing classification models with high-dimensional data. It facilitates the identification and elimination of redundant or irrelevant features, thereby reducing model complexity and enhancing predictive accuracy. The success of a classification model largely depends on the quality of the selected features. Accordingly, feature selection is performed during data preparation, prior to data processing, to … …Journal of Computer Science, Vol. 22, Issue 8 · 31 Aug 2026 · 774 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart and Secure Anomaly Detection in IoT Using Quantum Inspired Kernel Ensemble LearningAbrar M. AlajlanThe exponential growth of the Internet of Things networks has greatly increased the attack surface for cyber threats, necessitating the use of strong and perceptive anomaly detection systems. Traditional intrusion detection systems often struggle with high false alarm rates, limited generalization, and inefficiency in processing high-dimensional heterogeneous IoT data. To address this issue, a novel Hybrid Quantum-Enhanced Kernel Ensemble Learning … …Journal of Computer Science, Vol. 22, Issue 3 · 12 Mar 2026 · 1,762 accessesView PDFCite