A Novel Soil Profile Feature Reduction Model using Principal Component Analysis
نویسندگان
چکیده
منابع مشابه
Feature Dimension Reduction of Multisensor Data Fusion using Principal Component Fuzzy Analysis
These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. O...
متن کاملFeature reduction of hyperspectral images: Discriminant analysis and the first principal component
When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...
متن کاملModelling of some soil physical quality indicators using hybrid algorithm principal component analysis - artificial neural network
One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undistur...
متن کاملfeature reduction of hyperspectral images: discriminant analysis and the first principal component
when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...
متن کاملClustering and Feature Selection using Sparse Principal Component Analysis
In this paper, we use sparse principal component analysis (PCA) to solve clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to int...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Indian Journal of Science and Technology
سال: 2015
ISSN: 0974-5645,0974-6846
DOI: 10.17485/ijst/2015/v8i1/84111