On Optimal Allocation of Treatment/Condition Variance in Principal Component Analysis
نویسندگان
چکیده
منابع مشابه
Optimal sparse L1-norm principal-component analysis
We present an algorithm that computes exactly (optimally) the S-sparse (1≤S<D) maximum-L1-norm-projection principal component of a real-valued data matrix X ∈ RD×N that contains N samples of dimension D. For fixed sample support N , the optimal L1-sparse algorithm has linear complexity in data dimension, O (D). For fixed dimension D (thus, fixed sparsity S), the optimal L1-sparse algorithm has ...
متن کاملOptimal Solutions for Sparse Principal Component Analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. We formulate a new semidefinite relaxation to this pro...
متن کاملOptimal Mean Robust Principal Component Analysis
Principal Component Analysis (PCA) is the most widely used unsupervised dimensionality reduction approach. In recent research, several robust PCA algorithms were presented to enhance the robustness of PCA model. However, the existing robust PCA methods incorrectly center the data using the `2-norm distance to calculate the mean, which actually is not the optimal mean due to the `1-norm used in ...
متن کاملFast noise variance estimation by principal component analysis
Noise variance estimation is required in many image denoising, compression, and segmentation applications. In this work, we propose a fast noise variance estimation algorithm based on principal component analysis of image blocks. First, we rearrange image blocks into vectors and compute the covariance matrix of these vectors. Then, we use Bartlett’s test in order to select the covariance matrix...
متن کاملCompression of Breast Cancer Images By Principal Component Analysis
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
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: International Journal of Statistics and Probability
سال: 2018
ISSN: 1927-7040,1927-7032
DOI: 10.5539/ijsp.v7n4p50