high-dimensional feature space
The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
... from high-dimensional data or a large number of data ...the high-dimensional feature space by nonlinear mapping (Figure 1 below), which is the extension of PPCA in the kernel ...
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Algebraic perceptron in digital channel equalization
... Like the Support Vector Machine, the Algebraic Perceptron also achieves linear separation in the high dimensional feature space, but with reduced calculation requirem[r] ...
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Tree Kernel based SVM with Structured Syntactic Knowledge for BTG based Phrase Reordering
... a high dimensional feature space (Vapnik, 1995), in this paper we propose using convolution tree kernel (Haussler, 1999; Collins and Duffy, 2001) to explore the structured syntactic knowledge ...
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A Framework To Integrate Feature Selection Algorithm For Classification Of High Dimensional Data
... and high-dimensional social media data challenges traditional data mining tasks such as classification and clustering due to curse of dimensionality and scalability ...handle high-dimensional ...
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Study of Informative Value of Features in Rail Condition Monitoring
... a high dimensional feature space, where we search for the minimal enclosing ...data space, can separate into several components, each enclosing a separate cluster of ...
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A New Malware Classification Approach based on Statistical Feature
... However, high dimensional feature space brings a higher time overhead and one-sided feature can decreases the ...statistical feature based malware classification approach and a ...
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Effect of Purposeful Feature Extraction in High-dimensional Kinship Verification Problem
... eral feature extraction operators are ...several feature selection (FS) algorithms were introduced [13, 21, 22]. Feature selection tries to reduce the size of feature vector without losing ...
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Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
... analyzing high dimensional data, especially with a small number of ...samples. Feature extraction methods do not have decent performance in these ...and high dimensional data, exploring ...
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Neighborhood Component Feature Selection for High-Dimensional Data
... original feature space at the outset of learning and do not change during the learning ...original feature space may not be true in the weighted feature space, especially when ...
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Recurrent Kalman networks:factorized inference in high dimensional deep feature spaces
... Another family of approaches interprets encoder-decoder models as latent variable models that can be optimized effi- ciently by variational inference. They derive a correspond- ing lower bound and optimize it using the ...
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Title: Mining of High Dimensional Data using Feature Selection
... good feature subset is one that contains features highly allied with the target class but not interrelated with each other ...any feature already selected ...vector space where the classification ...
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Feature optimization in high dimensional chemical space: statistical and data mining solutions
... Structure and ligand-based virtual screening methods are widely employed in drug discovery process [1–4]. These methods are high dimensional and complex to analyze which pose some basic challenges. As the ...
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To Show the Multiple Alignment of the Image search Hash Efficiency
... embedding high-dimensional feature descriptors into a similarity- preserving Hamming space with a low ...the high-dimensional feature ...of feature cannot be ...
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Dynamic Feature Induction: The Last Gist to the State of the Art
... dynamic feature induction that keeps inducing high di- mensional features automatically until the fea- ture space becomes ‘more’ linearly ...Dynamic feature induction searches for the fea- ...
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A Survey on Clustered Feature Selection Algorithms for High Dimensional Data
... features. Feature subset selection methods are divided into Wrappers, Filters, Embedded and Hybrid ...the space of possible features and evaluate every subset by running a model on the ...Correlation-based ...
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SALIENT AREA DISCERNMENT VIA HIGH DIMENSIONAL COLOR TRANSFORM AND LOCAL SPECIAL PLATFORM
... Another structure for saliency calculation dependent on unearthly area is proposed in this paper. The calculation utilizes the band-pass separating in Fourier Transform (FT) space with a few data transfer ...
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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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Distance Preserving Embeddings for General n-Dimensional Manifolds
... is an absolute constant). For the second algorithm, we will follow Nash’s technique (Nash, 1954) more closely and apply Ψ maps iteratively in the same embedding space without the use of extra coordinates. At each ...
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Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
... of high-dimensional data hubs tend to be close to cluster centers, it would be interesting to explore whether this can be used to improve iterative clustering algorithms, like K-means or self-organizing ...
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MULTI-MODAL PALM VEINS-FACE BIOMETRIC AUTHENTICATION
... This paper studies the contribution of four different statistical approaches in recognizing persons through their palm veins and face images. These approaches are Gray-level Co-occurrence Matrix (GLCM), Run-Length Matrix ...
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