Although several approaches have been proposed to integrate the radius and margin information, most of them either require the form of the transformation matrix to be diagonal, or are nonconvex and computationally expensive. In this paper, we suggest a novel approximation for the radius of the ...
MATRIX TRANSFORMATION DEVICE AND FEATURE VECTOR TRANSFORMATION DEVICEPROBLEM TO BE SOLVED: To provide a matrix transformation device capable of approximately transforming a rotation transformation matrix into a sparse matrix having a small number of non-zero elements.ISHIKAWA KOTA...
Relevant component analysis has shown effective in metric learning. It finds a transformation matrix of the feature space using equivalence constraints. This paper explores this idea for constructing a feature metric (FM) and develops a novel semisupervised feature-selection technique for hyperspectral im...
(1)式为Non-local计算response的公式,简单说就是计算i与j的相似度,然后再去取j变换后的feature加到i的response中 (2)式为Gaussian Function (3)式为non-lcoal中对高斯公式的变换,讲元素先进行embed再计算(θ和φ为transformation matrix) (4)式为普通的dot-product (5)式为concatenate后再进行变换 ...
Through feature transformation, we can effectively approximate the whole graph without explicitly computing the similarity graph matrix, based on which a sequential learning method is proposed to learn the hash functions in a bit-wise manner. Experiments on two datasets with one million data points ...
Theoretical or Mathematical/ Bayes methods sparse matrices transforms/ doubly sparse factor model unifying feature transformation unsupervised learning method Bayesian inference procedure doubly automatic relevance determination factor loading matrix/ A0250 Probability theory, stochastic processes, and statistics A02...
Feature transformationtechniques reduce the dimensionality in the data by transforming data into new features.Feature selectiontechniques are preferable when transformation of variables is not possible, e.g., when there are categorical variables in the data. For a feature selection technique that is spec...
between two point clouds. Then, if the change in this step is smaller than threshold, the maximum space for detecting the corresponding point is reduced; the accuracy of registration is improved during the iteration process. Finally, the secondary rigid transformation matrix is obtained by SVD ...
It is shown that the propagation and transformation of a simply astigmatic Gaussian beam by an optical system with a characteristic ABCD matrix can be mode... Herloski,Marshall,Antos - 《Applied Optics》 被引量: 42发表: 1983年 A comparison study on the propagation characteristics of flattened ...
(LDA)9. These dimensionality reduction methods all extract spectral features of hyperspectral images through linear transformation. Among them, PCA is the most commonly used linear feature extraction method. In this method, the input data is transformed by using the transformation matrix, and the ...