منابع مشابه
Sparse Weighted Canonical Correlation Analysis
Given two data matrices X and Y , Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Y v. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. To this end, we propose a novel Sparse weighted cano...
متن کاملSparse Kernel Canonical Correlation Analysis
We review the recently proposed method of Relevance Vector Machines which is a supervised training method related to Support Vector Machines. We also review the statistical technique of Canonical Correlation Analysis and its implementation in a Feature Space. We show how the technique of Relevance Vectors may be applied to the method of Kernel Canonical Correlation Analysis to gain a very spars...
متن کاملStructured Sparse Canonical Correlation Analysis
In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-spars...
متن کاملCanonical sparse cross-view correlation analysis
Recently, multi-view feature extraction has attracted great interest and Canonical Correlation Analysis (CCA) is a powerful technique for finding the linear correlation between two view variable sets. However, CCA does not consider the structure and cross view information in feature extraction, which is very important for subsequence tasks. In this paper, a new approach called Canonical Sparse ...
متن کاملMinimax Estimation in Sparse Canonical Correlation Analysis
Canonical correlation analysis is a widely used multivariate statistical technique for exploring the relation between two sets of variables. This paper considers the problem of estimating the leading canonical correlation directions in high dimensional settings. Recently, under the assumption that the leading canonical correlation directions are sparse, various procedures have been proposed for...
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: Machine Learning
سال: 2010
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-010-5222-7