Dimensionality Reduction Evolution and Validation
نویسنده
چکیده
In this paper, proposing visualized and quantitative evaluation methods for validation dimensionality reduction techniques performance. Four well known techniques for dimensionality reduction evaluated, verify the capacity of generating a lower dimensional chemical space with minimum information error. Real chemical database used to generate a sample with specific structure as an input to evolution process. The evaluation is performed in two ways: first visually checking the sample structure, second measuring the local and global distance based error rate that are trained on the low-dimensional data representation. All the evaluation have been done for the four dimensional reduction techniques then result shows weather “trustable” to technique that likely preserves properties with low generalization error and “Un-trustable” which does not preserve the properties.
منابع مشابه
2D Dimensionality Reduction Methods without Loss
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (...
متن کاملDiagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms
Objective: Diabetes is one of the most common metabolic diseases. Earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. Diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. Classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...
متن کاملA Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters
Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...
متن کاملImpact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images
Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...
متن کاملSupervised Nonlinear Dimensionality Reduction Based on Evolution Strategy
Most of the classifiers suffer from the curse of dimensionality during classification of high dimensional image and non-image data. In this paper, we introduce a new supervised nonlinear dimensionality reduction (S-NLDR) algorithm called supervised dimensionality reduction based on evolution strategy (SDRES) for both image and nonimage data. The SDRES method uses the power of evolution strategy...
متن کامل