Saliency Detection via the Improved Hierarchical Principal Component Analysis Method
نویسندگان
چکیده
منابع مشابه
Improved Distributed Principal Component Analysis
We study the distributed computing setting in which there are multiple servers,each holding a set of points, who wish to compute functions on the union of theirpoint sets. A key task in this setting is Principal Component Analysis (PCA), inwhich the servers would like to compute a low dimensional subspace capturing asmuch of the variance of the union of their point sets as possi...
متن کاملPrincipal Component Analysis vs. Independent Component Analysis for Damage Detection
In previous works, the authors showed advantages and drawbacks of the use of PCA and ICA by separately. In this paper, a comparison of results in the application of these methodologies is presented. Both of them exploit the advantage of using a piezoelectric active system in different phases. An initial baseline model for the undamaged structure is built applying each technique to the data coll...
متن کاملSparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis. We discuss a robust and scalable algorithm for computing sparse principal component analysis. Specifically, we model SPCA as a matrix factorization problem with orthogonality constraints, and develop specialized optimization algorithms that partially minimize a subset of the variables (varia...
متن کاملTensor principal component analysis via convex optimization
This paper is concerned with the computation of the principal components for a general tensor, known as the tensor principal component analysis (PCA) problem. We show that the general tensor PCA problem is reducible to its special case where the tensor in question is supersymmetric with an even degree. In that case, the tensor can be embedded into a symmetric matrix. We prove that if the tensor...
متن کاملSparse principal component analysis via random projections
We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix. Unlike most alternative approaches, our algorithm is non-iterative, so is not vulnerable to a bad choice of initialisation. Our theory provides great detail on the statistical and computational trade-of...
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: Wireless Communications and Mobile Computing
سال: 2020
ISSN: 1530-8669,1530-8677
DOI: 10.1155/2020/8822777