Skin Lesion Feature Vector Space with a Metric to Model Geometric Structures of Malignancy for Classification

2012 ◽  
pp. 285-297 ◽  
Author(s):  
Mutlu Mete ◽  
Ye-Lin Ou ◽  
Nikolay Metodiev Sirakov
Keyword(s):  
Skin Lesion ◽  
Vector Space ◽  
Feature Vector ◽  
Geometric Structures ◽  
Feature Vector Space

Chinese Semantic Word-Formation Analysis Using FKP-MCO Classifier Based on Layered and Weighted GED

2013 ◽  
Vol 284-287 ◽  
pp. 3044-3050 ◽  
Author(s):  
Guang Xia Gao ◽  
Zhi Wang Zhang ◽  
Shi Yong Kang
Keyword(s):  
Vector Space ◽  
Large Scale ◽  
Semantic Analysis ◽  
Feature Vector ◽  
Class Imbalance ◽  
Weighted Graph ◽  
Predictive Performance ◽  
Word Formation ◽  
Support Vector ◽  
Feature Vector Space

For Chinese information processing, automatic classification based on a large-scale database for different patterns of semantic word-formation can remarkably improve the identification for the unregistered word, automatic lexicography, semantic analysis, and other applications. However, owing to noise, anomalies, nonlinear characteristics, class-imbalance, and other uncertainties in word-formation data, the predictive performance of multi-criteria optimization classifier (MCOC) and other traditional data mining approaches will rapidly degenerate. In this paper we put forward an novel MCOC with fuzzification, kernel, and penalty factors (FKP-MCOC) based on layered and weighted graph edit distance (GED): firstly the layered and weighted GEDs between each semantic word-formation graph and prototype graphs are calculated and used for the dissimilarity measure, then the normalized GEDs are embedded into a new feature vector space, and FKP-MCO classifier based on the feature vector space is built for predicting the patterns of semantic word-formation. Our experimental results of Chinese word-formation analysis and comparison with support vector machine (SVM) show that our proposed approach can increase the separation of different patterns, the predictive performance of semantic pattern of a new compound word.

scholarly journals Patent Analytics Based on Feature Vector Space Model: A Case of IoT

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45705-45715 ◽  
Author(s):  
Lei Lei ◽  
Jiaju Qi ◽  
Kan Zheng
Keyword(s):  
Vector Space ◽  
Feature Vector ◽  
Vector Space Model ◽  
Space Model ◽  
Feature Vector Space

A Kind of Self-Constructed Category Dictionary in Chinese Text Classification

2014 ◽  
Vol 644-650 ◽  
pp. 2206-2210
Author(s):  
Kun Zhou ◽  
Ya Ping Dai ◽  
Feng Gao ◽  
Ji Hong Zou
Keyword(s):  
Vector Space ◽  
Chinese Text ◽  
Text Classification ◽  
Feature Vector ◽  
Vector Space Model ◽  
Recall Rate ◽  
Support Vector ◽  
Space Model ◽  
Chinese Text Classification ◽  
Feature Vector Space

By means of word-segmentation technology in TRIP database and each word that appears in a database will be account in detail, a kind of self-constructed category dictionary (SCC-dictionary) in Chinese text classification is proposed. For solving high dimension and sparseness problem exit in vector space model, a four-dimensional feature vector space model (FFVSM) is presented in this paper. With Support Vector Machine (SVM) algorithm, the text classifier is designed. Experimental results show there are two achievements in this paper: first, SCC-dictionary can replace the artificial-written dictionary with the same effect; second, the FFVSM will not only reduce the computing load than high-dimensional feature vector space model, but also keep the precision of classification as 86.87%, recall rate as 95.12%, and F1 value as 90.81%.

scholarly journals Transformation of measurement uncertainties into low-dimensional feature vector space

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
A. Alexiadis ◽  
S. Ferson ◽  
E. A. Patterson
Keyword(s):  
Measurement Uncertainty ◽  
Vector Space ◽  
Model Updating ◽  
Feature Vector ◽  
Southern Oscillation ◽  
Dimensional Space ◽  
Principal Component ◽  
Global Ocean ◽  
Low Dimensional ◽  
Feature Vector Space

Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for postprocessing or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples, Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose the global ocean temperature data. The uses of the method range from supporting decision-making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterization of the El Niño Southern Oscillation.

scholarly journals GRADED GEOMETRIC STRUCTURES UNDERLYING F-THEORY RELATED DEFECT THEORIES

2013 ◽  
Vol 28 (21) ◽  
pp. 1350106
Author(s):  
V. K. OIKONOMOU
Keyword(s):  
Vector Space ◽  
Dimensional Space ◽  
Composite Fiber ◽  
Fiber Bundles ◽  
Canonical Bundle ◽  
Geometric Structures ◽  
Yang Mills ◽  
Zero Modes ◽  
Fermionic Fields ◽  
Related Defect

In the context of F-theory, we study the related eight-dimensional super-Yang–Mills theory and reveal the underlying supersymmetric quantum mechanics algebra that the fermionic fields localized on the corresponding defect theory are related to. Particularly, the localized fermionic fields constitute a graded vector space, and in turn this graded space enriches the geometric structures that can be built on the initial eight-dimensional space. We construct the implied composite fiber bundles, which include the graded affine vector space and demonstrate that the composite sections of this fiber bundle are in one-to-one correspondence to the sections of the square root of the canonical bundle corresponding to the submanifold on which the zero modes are localized.

scholarly journals Feasibility of polarized all-sky imaging for aerosol characterization

2013 ◽  
Vol 6 (7) ◽  
pp. 1845-1854 ◽  
Author(s):  
A. Kreuter ◽  
M. Blumthaler
Keyword(s):  
Stokes Parameter ◽  
Feature Vector ◽  
Index Of Refraction ◽  
Zernike Polynomials ◽  
Frame Work ◽  
The Individual ◽  
Aerosol Types ◽  
Feature Vector Space ◽  
Parameter Distributions ◽  
Aerosol Characterization

Abstract. In this study, we investigate the method of polarized all-sky imaging with respect to aerosol characterization. As a technical frame work for image processing and analysis, we propose Zernike polynomials to decompose the relative Stokes parameter distributions. This defines a suitable and efficient feature vector which is also appealing because it is independent of calibration, circumvents overexposure problems and is robust against pixel noise. We model the polarized radiances of realistic aerosol scenarios and construct the feature vector space of the key aerosol types in terms of the first two principal components describing the maximal variances. We show that, using this representation, aerosol types can be clearly distinguished with respect to fine and coarse mode dominated size distribution and index of refraction. We further investigate the individual influences of varying aerosol properties and solar zenith angle. This suggests that polarized all-sky imaging may improve aerosol characterization in combination with sky scanning radiometers of the existing Aerosol Robotic Network (AERONET) especially at low aerosol optical depths and low solar zenith angles.

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