Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.
For example, in @cite_27 and @cite_22 , the authors construct an integrative network to fuse multiple similarity networks via an iterative scaling approach. @cite_29 ,the authors integrated feature ranking and feature variation as feature weights for weighted similarity fusion. These unsupervised methods have good flexibility, however without any supervision, unreliable results could be generated.
@cite_27 @cite_29 @cite_22
2012982311 2614256707 2917580522 2741640202
Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion techniques simply concatenate a pair of different features or use canonical correlation analysis based methods for joint dimensionality reduction in the feature space. However, how to fuse multiple features in a generalized way is still an open problem. In this paper, we reformulate the multiple feature fusion as a general subspace learning problem. The objective of the framework is to find a general linear subspace in which the cumulative pairwise canonical correlation between every pair of feature sets is maximized after the dimension normalization and subspace projection. The learned subspace couples dimensionality reduction and feature fusion together, which can be applied to both unsupervised and supervised learning cases. In the supervised case, the pairwise canonical correlations of feature sets within the same classes are also counted in the objective function for maximization. To better model the high-order feature structure and overcome the computational difficulty, the features extracted from the same pattern source are represented by a single 2D tensor. The tensor-based dimensionality reduction methods are used to further extract low-dimensional discriminative features from the fused feature ensemble. Extensive experiments on visual data classification demonstrate the effectiveness and robustness of the proposed methods. In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets. Features from multiple scales can greatly benefit the semantic edge detection task if they are well fused. However, the prevalent semantic edge detection methods apply a fixed weight fusion strategy where images with different semantics are forced to share the same weights, resulting in universal fusion weights for all images and locations regardless of their different semantics or local context. In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively. This is achieved by a proposed weight learner to infer proper fusion weights over multi-level features for each location of the feature map, conditioned on the specific input. In this way, the heterogeneity in contributions made by different locations of feature maps and input images can be better considered and thus help produce more accurate and sharper edge predictions. We show that our model with the novel dynamic feature fusion is superior to fixed weight fusion and also the naive location-invariant weight fusion methods, via comprehensive experiments on benchmarks Cityscapes and SBD. In particular, our method outperforms all existing well established methods and achieves new state-of-the-art. Different kinds of features hold some distinct merits, making them complementary to each other. Inspired by this idea an index level multiple feature fusion scheme via similarity matrix pooling is proposed in this paper. We first compute the similarity matrix of each index, and then a novel scheme is used to pool on these similarity matrices for updating the original indices. Compared with the existing fusion schemes, the proposed scheme performs feature fusion at index level to save memory and reduce computational complexity. On the other hand, the proposed scheme treats different kinds of features adaptively based on its importance, thus improves retrieval accuracy. The performance of the proposed approach is evaluated using two public datasets, which significantly outperforms the baseline methods in retrieval accuracy with low memory consumption and computational complexity.