In this article, we propose a novel $\\\ell _{2,p}$-norm and Mahalanobis distance-based FCM model, abbreviated as LM-FCM, which can help FCM improve the ability of tackling ellipsoidal clusters and outliers. Then, in order to reduce computational complexity, we propose a more simplified ...
metrics import l2_norm from ...misc.regularization import L2Regularization, compute_penalty_matrix from ...representation import FData from ...representation._typing import ArrayLike from ...representation.basis import Basis, FDataBasis from ...representation.grid import FDataGrid Function = TypeVar(...
However, for pattern recognition, since pattern vec- tors are often normalized by their norm, they are on a hyper-spherical surface. Therefore, we have to study a normal distribution in a non- Euclidean space. Here, we provide the new concept of geometrically lo- cal isotropic independence ...
l1w = white\l1; % Whiten the direction vector l1w = l1w/norm(l1w); % Normalize the direction vector d = l0(2)*l1(1)-l0(1)*l1(2); % Distance from (0,0) to line Where Mean and Sigma are the moments of your 2D Gaussian, white is the whitening transformation, l0 is a po...
investigation, we analyze the effectiveness of three well-known similarity measures including the typical l 1 norm, l 2 norm, and Mahalanobis distance. We... J Shi,A Samal,D Marx - 《Computer Vision & Image Understanding》 被引量: 183发表: 2006年 Targeting specific facial variation for diffe...
Finally, the diagonal selection matrix is learned through least squares with non-negative L2-norm regularization. Experiments on two datasets in scene recognition show the effectiveness and efficiency of our approach. 展开 关键词: Metric learning Mahalanobis metric regularized LDA non-negative L2-norm ...
(4) 2 We consider Euclidean norm in this paper. 162 K. Misztal and J. Tabor Proof. By applying the transformation described in Remark 1 we can reduce our reasoning to the case when E = B(0, R). Then by (3) BΣE (0, 2) = {x : x ΣE ≤ 2} = {x : x 2 ΣE ≤ 4...
Reidentification by relative distance comparison 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence Robust principal component analysis with non-greedy ℓ<inf>1</inf>-norm maximization 2011, IJCAI International Joint Conference on Artificial IntelligenceView...
L<inf>1</inf>-norm loss based twin support vector machine for data recognition 2016, Information Sciences Citation Excerpt : Support vector machine (SVM) is an excellent kernel-based tool for classification and regression [1,45,46]. Within a few years after its introduction SVM has already ou...
色图像正则化(normlized) 的马氏距离(Mahalanobis distance)和模糊C 均值(fuzzy C鄄means)聚类抠图算 法,为方便表述本文将此算法简记为N鄄M鄄FCM;第3 节将本文算法与其他三种抠图算法的抠图结果进行 对比,该算法的应用性也在本节进行了相关介绍;第 4 节对本文进行总结. 1摇 马氏距离及其计算 马氏距离是由 Maha...