High Dimensionality
An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality
... The cluster analysis method is one of the critical methods in data mining; this method of clustering algorithm will manipulate the clustering results directly. This paper proposes an Advanced Clustering Algorithm in ...
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High-Dimensionality Graph Data Reduction Based on Proposing A New Algorithm
... However, high-dimensionality of the graph data remains a difficult task, mainly because the analysis system is not used to dealing with large graph ...graph-based dimensionality reduction approaches ...
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Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data
... the high dimensionality in data [2], therefore, there is a need for computation methods to help improve the classification of cancers using microarray gene ...
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On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality
... It is noted that there exists much previous work in addressing the problems considered in this paper, in addition to the related work mentioned in the Introduction (we thank the reviewers for bringing our attention to ...
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High dimensionality carrierless amplitude phase modulation technique for radio over fiber system
... The interest in CAP lies in its well-known spectral efficiency (SE), which makes it very competitive with OFDM. Recently, CAP has gained attention in optical communication due to its potentially high SE (Ingham ...
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High Dimensionality Effects on the Efficient Frontier: A Tri Nation Study
... expected returns. We have considered the same set of µ’s and Σ’s, for each individual country to keep the results comparable. It can be concluded from the relative positions of the corrected and uncorrected efficient ...
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A Review on Big Data Analytics in Business
... Data complexity: It refers to the specific features of data including huge size, high dimensionality, and extreme imbalance, online and real-time interaction and p[r] ...
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A Review Paper on Feature Selection Methodologies and Their Applications
... Metadata such as filename, author, size, date, track length and genres are the common features used to classify and retrieve genre documents. On the basis of these data, the classification is infeasible, so the feature ...
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Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression
... in high-dimensional classification, because of the problem of non-invertible matrices in estimating model ...the high-dimensionality of data, we introduce two new ...for high-dimensional ...
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Tests for High Dimensional Generalized Linear Models
... the high dimensionality can insert adverse influence on the test of Goeman et ...the high dimensional effects, and hence lead to better performances in terms of more accurate size approximation and ...
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Bayesian Posterior Approximation via Greedy Particle Optimization
... Results for other datasets are shown in Table 1, where we fixed the wall clock time and applied MMD-FW and SVGD within that period. SP did not work well because of the high dimensionality so its results are ...
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On Dimensionality Reduction of Data
... by high-dimensional data is trivial to state, but not so simple to ...of dimensionality” has plagued researchers in machine learning and other fields for ...the high-dimensional data with low- ...
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Big Data Solution for Predicting the Risk of Readmission for the Patients
... Predictive models are appropriate for various kinds of clinical risk assessments in health care domain. Clinical risk calculators and risk assessment tools provide information about a person's chance of having a disease ...
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Dynamical properties and extremes of Northern Hemisphere climate fields over the past 60 years
... the high dimensionality of atmospheric dynamics, the Northern Hemisphere sea-level pressure and temperature fields can on average be described by roughly 20 degrees of freedom; (ii) the precipitation field ...
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A Survey on Classification Techniques in Internet Environment Akarshika Rawat, Ankita Choubey
... In 2010, Tinghua Wang presents an effective feature selection method for support vector machine (SVM). Unlike the traditional combinatorial searching method, features election is translated into the model selection of ...
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An Efficient Dimensionality Reduction Approach for Small-sample Size and High-dimensional Data Modeling
... The traditional analytical methods are not a very good solution to solve the curse of dimensionality dilemma caused by the high dimensionality. When the small- sample modeling problem is taken into ...
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Unsupervised Dimensionality Reduction for High-Dimensional Data Classification
... however, high data dimensionality occurs, posing considerable challenges to ...of high dimensionality, these methods are not effective anymore due to space ...classifying ...
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On the Role of Dimensionality Reduction
... of dimensionality reduction comes from being a necessary step in many real-world applications such as text categorization, customer relationship management, gene expression microarray analysis, intrusion ...
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A 2-Dimensional Gabor-Filters for Face Recognition System: A Survey
... the high dimensionality of features results into large computational complexity of this ...efficient dimensionality reduction method which involves the application of embedded hybridization of linear ...
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Comparative Analysis of Dimensionality Reduction Techniques for Machine Learning
... estimation, dimensionality reduction (DR) plays a tremendous important role not only because features of these two topics are hard to define and usually of really high dimensionality, but also ...
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