very high dimensional data space
Very Fast Interactive Visualization of Large Sets of High-dimensional Data
... large high-dimensional data ...original high-dimensional data Y, which are sufficient to obtain a proper data embedding in ...each data vector in Y only indices of a ...
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Polynomial Kernel Function based Support Vectors for Data Stream Clustering
... of data sets of arbitrary shape, no need for specifying the number of clusters, fewer parameters, and easy treatment of high dimensional ...outlier data doesn’t optimized using linear kernel ...
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Maximizing Biochromatic Reverse Nearest Neighbors In Unsupervised Outlier Detection
... Outlier detection is to analysis high dimensional space in order to detect duplication data in unsupervised method. The actual challenges posed by the “curse of dimensionality” differ from the ...
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On the Performance of Latent Semantic Indexing-based Information Retrieval
... the data are lying in a high dimensional space, it is beneficial to reduce the dimension of the data to improve efficiency and accuracy of data analysis [ 8, 9 ] ...the ...
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Study of Informative Value of Features in Rail Condition Monitoring
... machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing ...to data space, can separate into ...
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Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
... forming data to a high dimensional space, but training and testing large data sets is often time ...larger data sets using linear SVM without ...mapped data and ...
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Feature optimization in high dimensional chemical space: statistical and data mining solutions
... PCA was able to reduce the number of molecular descriptors which were used during the virtual screen- ing process. This study emphasized that screening car- ried out with a reduced number of descriptors resulted in ...
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HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
... brain data and movie data onto a three dimensional space, although the overall “shapes” of those datasets might be similar, there would be no inherent reason for the datasets to ...the ...
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SALIENT AREA DISCERNMENT VIA HIGH DIMENSIONAL COLOR TRANSFORM AND LOCAL SPECIAL PLATFORM
... (FT) space with a few data transfer capacities that can speak to mindful districts on the ...the data transmission the more surface level saliency can be found, and with the littler transfer speeds ...
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RECURSIVE ANTIHUB2 OUTLIER DETECTION IN HIGH DIMENSIONAL DATA
... in high-dimensional data identifies seven issues in addition to distance concentration: noisy attributes definition of reference sets, bias (comparability) of scores, interpretation and contrast of ...
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Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model
... probabilistic data association filter (JPDAF) ...the high-dimensional learning datasets may increase classifica- tion and computation ...latent space for data ...
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Booster in High Dimensional Data Classification
... of high dimensional data is becomingmore common in many practical applicationssuch as data mining, machine learning and microarraygene expression data ...availablemicroarray data ...
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REAL TIME FACIAL EXPRESSION RECOGNITION IN THE PRESENCE OF ROTATION AND PARTIAL OCCLUSIONS
... Hilbert space clustering algorithm. For each sparse and dense high dimensional data, the mapping of data space into Hilbert space is performed using mapping ...combined ...
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Large-Scale Interpretable Multi-View Learning for Very High-Dimensional Problems with Application to Multi-Omic Data
... In high-dimensional settings, to avoid overfitting and improve interpretability, regularization terms are added which results in sparse additive linear ...
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Parallelization of Support Vector Machines
... algorithm data points are mapped from data space to a high dimensional feature space using a Gaussian ...to data space, where it forms a set of contours that ...
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Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
... Returning to Theorem 12 and the value of β from Equation 19, as previously discussed, β > 0 signifies that the position of a vector component value makes a difference when computing dis- tances between vectors, ...
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Segmentation of Dynamic PET Images with Kinetic Spectral Clustering
... the data into a high dimensional space and then identifying the clusters in a low-dimensional ...brain data, no assumption was made on the anatomical structures nor on the ...
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A new approach for data visualization problem
... large data of multiple dimensions into a smaller, more manageable set with special ...for data reduction and ...of data is conducted by multiplying each component of the original vector with a ...
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Design and Implementation of Sensitive Information Security Model based on Term Clustering
... enormous data and information securely and frequently via Internet is very common and demanded in today‟s fast track scenario of ...contain very important and sensitive information or data or ...
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To Show the Multiple Alignment of the Image search Hash Efficiency
... large-scale data spaces, by embedding high-dimensional feature descriptors into a similarity- preserving Hamming space with a low ...the high-dimensional feature ...
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