We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove optimal, non-asymptotic lower and upper bounds on the minimax estimation error for the leading eigenvector when it belongs to an lq ball for q ∈ [0, 1]. Our bounds are sharp in p and n for all q ∈ [0, 1] over a wide cla...

spatial patterns are useful descriptors of the horizontal structure in a plant population and may change over time as the individual components of the population grow or die out. but, whether this is the case for desert woody annuals is largely unknown. in the present investigation, the variations in spatial patterns of tribulus terrestris during different pulse events in semi-arid area of the ...

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...

In this project you will explore the use of Principle Component Analysis (PCA) and Probabilistic PCA (PPCA). PPCA is closely-related to factor analysis, which is described in chapter 14 of your text. Our application is face recognition, following on the work of Moghadden and Pentland. A minimum version of this project would involve reading chapter 14 and conducting a face recognition experiment...

This paper mainly address the image compression by using Principal component analysis (PCA) and JPEG. Image compression is the method of converting data file into smaller compact files for efficiency of storage and transmission. The main objective of compression of image is to reduce redundancy of bits in the image in order to store and transmit data in an effective way. Image compression techn...

This paper studies the application of principal component analysis, multiple polynomial regression, and artificial neural network ANN techniques to the quantitative analysis of binary mixture of dye solution. The binary mixtures of three textile dyes including blue, red and yellow colors were analyzed by PCA-Multiple polynomial Regression and PCA-Artificial Neural network PCA-ANN methods. The o...