The experimental results show that our algorithm outperforms other baseline algorithms significantly in terms of the assessment metric geometric mean (G-mean), especially in the presence of data irregularities.Ling WangRocket Force University of Engineering Xi’an ChinaHongqiao Wang...
The finished graphs were fused into the public graph to obtain the geometric structure among samples. In [57], the learning latent embedding for IMC (LLE-IMC) framework projected views into a consensus latent space. It then performed weighted alignment of projection matrices and imposed l2,1 ...
Although it requires simple computations, provides good performance on linear classification tasks and offers a suitable environment for active learning st
stress-*: rename GEOMETRIC_MEAN and HARMONIC_MEAN metric macros May 10, 2024 stress-af-alg-defconfigs.h stress-af-alg-defconfigs: re-order and remove blank lines Jan 2, 2024 stress-af-alg.c stress-af-alg: add some sanity checks on digest and info args May 20, 2024 stress-affinity.c...
In this paper, we consider performance metrics, such as accuracy on test dataset and mean accuracy of fivefold cross validation on training dataset, as well as the generalization metric geometric difference (GD) introduced in Section 2.4. GD measures the difference between two kernel matrices, so...
In the second phase, the spatial analysis, employing the cross-correlation technique, was carried out to compute the cross-correlation of mean sea level between the attractive and neighbor-measured locations. The results revealed that the LS-SVM model was superior to the other machine learning ...
On the other hand, our method, which encodes the geometric information of the nucleus centroids and patch centers, produces much better overall performance than others. The DV-3 also gives reasonable results with faster computational time. The boxplot in Fig. 7.3 provides detailed comparisons in ...
The previous works, which is described in this section, mainly focused on geometric interpretations of the embedded space obtained by PTE and its metric structure. In this paper, on the other hand, we focus on the practical application of this analysis, which requires a dictionary construction an...
Basically, manifold is a geometry concept, which could be roughly described as the hyper-surface spanned by data points in high- dimensional space, which visually reflects the geometric properties of data distributions. Suitable (explicit or im- plicit) transform should be able to embed data ...
In this paper, we propose a new feature selection method called kernel fisher discriminant analysis and regression learning based algorithm for unsupervised feature selection. The existing feature selection methods are based on either manifold learning or discriminative techniques, each of which has some ...