Sparse linear discriminant analysis for multiview structured data
نویسندگان
چکیده
منابع مشابه
Sparse Linear Discriminant Analysis by Thresholding for High Dimensional Data
In many social, economical, biological, and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the probability distribution of the variables is usually unknown, the rule of classification is constructed using a training sample. The well-known linear discriminant analysis (LDA) works well for the situ...
متن کاملSparse Uncorrelated Linear Discriminant Analysis
In this paper, we develop a novel approach for sparse uncorrelated linear discriminant analysis (ULDA). Our proposal is based on characterization of all solutions of the generalized ULDA. We incorporate sparsity into the ULDA transformation by seeking the solution with minimum `1-norm from all minimum dimension solutions of the generalized ULDA. The problem is then formulated as a `1-minimizati...
متن کاملMultiview Fisher Discriminant Analysis
CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by extension CCA, does not use label information. Fisher Discriminant Analysis uses label information to find informative projections, which can be more informative in supervised learning settings. We show ...
متن کاملUnderstanding and Evaluating Sparse Linear Discriminant Analysis
Linear discriminant analysis (LDA) represents a simple yet powerful technique for partitioning a p-dimensional feature vector into one of K classes based on a linear projection learned from N labeled observations. However, it is well-established that in the high-dimensional setting (p > N ) the underlying projection estimator degenerates. Moreover, any linear discriminate function involving a l...
متن کاملCommunication-efficient Distributed Sparse Linear Discriminant Analysis
We propose a communication-e cient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size N into m machines, and estimates a local sparse LDA estimator on each machine using the data subset of size N/m. After the distributed estimation, our method aggregates the debiased local estimators from m machines...
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: Biometrics
سال: 2021
ISSN: 0006-341X,1541-0420
DOI: 10.1111/biom.13458