input data dimensionality reduction
Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space
... the reduction of the number of parameters and the elimination of technical knowledge required to creatively equalise the input ...with data extracted from a semantic equalisation plug-in, using the ...
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Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
... the input neighborhoods as described in Section ...of data points and K is the number of mixture components used to estimate the metric; this choice roughly means that for well-separated mixture components, ...
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Treelets A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data
... a data set. For example, in DNA microar- ray data, it can be quite difficult to detect small sets of highly correlated genes from a global PCA ...noisy input data, it constructs an optimal ...
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High-Dimensionality Graph Data Reduction Based on Proposing A New Algorithm
... these data are presented in high-dimensional ...as input for other ...such data efficiently. In this case, dimensionality reduction is used to map a set of high- dimensional data ...
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Distributed dimensionality reduction of industrial data based on clustering
... the data being reduced dimensionality directly or the proposed ...of dimensionality reduction. The data after being reduced dimensionality as an input data of SVM, ...
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Identifying MicroRNA Precursors Using Linear Dimensionality Reduction With Explicit Feature Mapping
... maps input samples to a low-dimensional Euclidean inner product space, in which the inner product of any two mapped data is equivalent to a Gaussian radial basis function kernel of two input ...the ...
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Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high dimensional input/output spaces
... on data sampled at a set of locations in an ...the input fields is that the dimension of the stochastic input space is as high as the number of grid points in the computational ...
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Robustness in Dimensionality Reduction
... The first group of methods achieves the robustness by replacing the sample covariance matrix by a robust alternative. This approach dates back to Maronna [1976] and Campbell [1980] who proposed using affine equivariant ...
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Dimensionality Reduction and Data Partitioning with Feature Hybridization Scheme
... the input to build forecasting models using existing methods such as support vector machines or LASSO and 3) Time series-based methods, where methods such as autoregressive models are used to model the temporal ...
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Comparative Analysis of Dimensionality Reduction Techniques
... the data for prediction. First the data matrix is taken as ...original data X, then the covariance is calculated for matrix X, then Eigen vector and Eigen values are ...
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Dimensionality Reduction with Multilingual Resource
... high dimensionality and contain too much noise. The high dimensionality causes high computational and memory require- ments while noise in the vectors degrades the sys- tem ...
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Novel Dimensionality Reduction Method for Symbolic Data using Coefficient of Variation
... This is the final similarity between i and k. If there are 2*m interval type values between samples i and k, then we get m Sj’s between any pair of samples and thus it reduces the dimension by 50%. The coefficient of ...
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Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
... recognition data set (see the top row of Figure 5), LFDA nicely separates samples in different classes from each other, and at the same time, it clearly preserves within-class ...Iris data set (see the top ...
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1. Survey on the principal challenge of text mining
... in input data is too large to be processed then the input data will be transformed into a reduced representation set of ...the dimensionality of data by transforming the original ...
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Factor regression for dimensionality reduction and data integration techniques with applications to cancer data
... In this thesis we will focus on cancer-related gene expression data as our main moti- vating application. Cancer is one of the most studied pathological systems and one of the leading causes of morbidity and ...
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An Actual Survey of Dimensionality Reduction
... methods published in various areas, such as signal processing and statistical machine learning literature. This actual survey is organized as follows: Section 2 reviews the linear nature of Principal component analysis ...
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Dimensionality reduction and class prediction algorithm with application to microarray Big Data
... Big Data is still how to efficiently process ...dimension data, are inadequate when analyzing Big ...dimension reduction and variables selection play pivotal roles in analyzing high dimension ...
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On Dimensionality Reduction of Data
... high-dimensional data is trivial to state, but not so simple to ...of dimensionality” has plagued researchers in machine learning and other fields for ...any data entry, as we increase the number of ...
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Dimensionality reduction of quality of life indicators
... Selecting indicators for assessing the quality of life at the regional level is not unambigous. Currently, there are no precisely defi ned indicators that would give comprehensive information about the quality of life on ...
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Robust Speaker Recognition for Large-scale data using PFA Dimensionality Reduction
... large-scale data set ...dependent dimensionality reduction technique is employed to reduce the dimension of pitch and pitch strength based feature vectors, known as Principle Factor Analysis ...based ...
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