section gives the conclusion and provides future directions. related work first, we discuss the problem to be solved and define the notations that are used in the rest of the paper. then, we introduce two mf-based methods, which are closely related to our model: a traditional one ...
Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although ma
Since Equation (16) contains multiple variables, it can be solved by alternate iterative update methods. When solving one of the variables, keep the other variables unchanged. Accordingly, the solution process can be broken down into the following form. (1) Update U when keeping F and A ...
Figure 1. Examples of multi-label remote sensing images collected by QuickBird satellite and their annotations. (a) Annotations: Giza Pyramids and Egypt; (b) annotations: Train Station, Zurich and Switzerland. Figure 2. Flowchart of Multi-Label Classification based on Low Rank Representation (MLC...
(a) All modalities of features in a testing image; (b), (c), and (d) are examples of coefficient sets considering sparse (MTJSRC), low-rank, and sparse + low-rank (MTJSLRC) respectively. The coefficient sets learnt by MTJSLRC are jointly sparse, and a few (but the same) ...
Figure 1. Examples of multi-label remote sensing images collected by QuickBird satellite and their annotations. (a) Annotations: Giza Pyramids and Egypt; (b) annotations: Train Station, Zurich and Switzerland. Figure 2. Flowchart of Multi-Label Classification based on Low Rank Representation (MLC...
However, the problem can be solved by the regularization function of the proposed model. Because SVM-RL can minimize the approximate Hamming Loss with less model complexity, it is easy to control the generalization error by the mechanism; thus, the generalization ability of SVM-RL can be ...
However, the problem can be solved by the regularization function of the proposed model. Because SVM-RL can minimize the approximate Hamming Loss with less model complexity, it is easy to control the generalization error by the mechanism; thus, the generalization ability of SVM-RL can be ...
The ℓ1ℓ1-regularized formulation was solved with L1_LS package for Matlab [36]. The resulting image is shown in Figure 8e. While a small value for 𝜆λ yields an image dominated by noise, such as that of Figure 8d, larger values cause the image to be too sparse, suppressing so...
Equation (7) can be solved by ADMM [19] to obtain the coefficient matrix Z. Afterwards, the coefficient matrix Z can be utilized for the construction of affinity matrix W = |Z| + ZT . Finally, spectral clustering can be applied to W for segmentation results. 3. Non-Convex Sparse and ...