Classification of Crystallographic Data Using Canonical Correlation Analysis
نویسندگان
چکیده
منابع مشابه
Classification of Crystallographic Data Using Canonical Correlation Analysis
A reliable and automatic method is applied to crystallographic data for tissue typing. The technique is based on canonical correlation analysis, a statistical method which makes use of the spectral-spatial information characterizing X-ray diffraction data measured from bone samples with implanted tissues. The performance has been compared with a standard crystallographic technique in terms of a...
متن کاملTissue segmentation and classification of MRSI data using canonical correlation analysis.
In this article an accurate and efficient technique for tissue typing is presented. The proposed technique is based on Canonical Correlation Analysis, a statistical method able to simultaneously exploit the spectral and spatial information characterizing the Magnetic Resonance Spectroscopic Imaging (MRSI) data. Recently, Canonical Correlation Analysis has been successfully applied to other type...
متن کاملUnsupervised analysis of fMRI data using kernel canonical correlation
We introduce a new unsupervised fMRI analysis method based on kernel canonical correlation analysis which differs from the class of supervised learning methods (e.g., the support vector machine) that are increasingly being employed in fMRI data analysis. Whereas SVM associates properties of the imaging data with simple specific categorical labels (e.g., -1, 1 indicating experimental conditions ...
متن کاملCanonical correlation analysis for functional data
Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coef...
متن کاملCanonical correlation analysis using within-class coupling
0167-8655/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.patrec.2010.09.025 q The work of O. Kursun was supported by Scienti nation Unit of Istanbul University under the grant YA ⇑ Corresponding author. Tel.: +90 212 473 7070/17 E-mail addresses: [email protected] (O. Kurs Alpaydin), [email protected] (O.V. Favorov). Fisher’s linear discriminant analysis (LDA) is one of the most ...
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: EURASIP Journal on Advances in Signal Processing
سال: 2007
ISSN: 1687-6180
DOI: 10.1155/2007/19260