نتایج جستجو برای: eigenvectors and gram
تعداد نتایج: 16834237 فیلتر نتایج به سال:
Solving linear systems is often the computational bottleneck in real-life problems. Iterative solvers are only option due to complexity of direct algorithms or because system matrix not explicitly known. Here, we develop a two-level preconditioner for regularized least squares involving feature data matrix. Variants this may appear machine learning applications, such as ridge regression, logist...
A real symmetric matrix of order n has a full set of orthogonal eigenvectors. The most used approach to compute the spectrum of such matrices reduces first the dense symmetric matrix into a symmetric structured one, i.e., tridiagonal matrices or semiseparable matrices. This step is accomplished in O(n 3) operations. Once the latter symmetric structured matrix is available, its spectrum is compu...
Antibacterial peptides are subject to broad research due to their potential application and the benefit they can provide for a wide range of diseases. In this work, a mathematical-computational method, called the Polarity Vector Method, is introduced that has a high discriminative level (>70%) to identify peptides associated with Gram (-) bacteria, Gram (+) bacteria, cancer cells, fungi, insect...
In this paper, we compare with the inverse iteration algorithms on PowerXCell 8i processor, which has been known as a heterogeneous environment. When some of all the eigenvalues are close together or there are clusters of eigenvalues, reorthogonalization must be adopted to all the eigenvectors associated with such eigenvalues. Reorthogonalization algorithms need a lot of computational cost. The...
Kernel principal component analysis (KPCA) is a widelyused statistical method for representation learning, where PCA is performed in reproducing kernel Hilbert space (RKHS) to extract nonlinear features from a set of training examples. Despite the success in various applications including face recognition, KPCA does not scale up well with the sample size, since, as in other kernel methods, it i...
The problem of constructing an orthogonal set of eigenvectors for a DFT matrix is well studied. An elegant solution is mentioned by Matveev in [1]. In this paper, we present a distilled form of his solution including some steps unexplained in his paper, along with correction of typos and errors using more consistent notation. Then we compare the computational complexity of his method with the m...
one of the most important number sequences in mathematics is fibonacci sequence. fibonacci sequence except for mathematics is applied to other branches of science such as physics and arts. in fact, between anesthetics and this sequence there exists a wonderful relation. fibonacci sequence has an importance characteristic which is the golden number. in this thesis, the golden number is observed ...
We study certain function algebras and their operator algebra completions on r-discrete abelian groupoids, the corresponding conditional expectations, maximal abelian subalgebras (masa) and eigen-functionals. We give a semidirect product decomposition for an abelian groupoid. This is done through a matched pair and leads to a C*-diagonal (for a special case). We use this decomposition to study ...
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