Learning features from multiple views has attracted much research attention in different machine learning tasks, such as multiclass and multilabel classification problems. In this paper, we propose a multiclass multilabel multiview learning framework with a linear computational cost where an example is associated with at least one label and represented by multiple information sources. We simultaneously analyze all features by learning an integrated projection matrix. We can also automatically select more important views for subsequent classifier to predict each class. As the proposed objective function is nonsmooth and difficult to solve, we apply a novel optimization method that converts the multiview learning problem to a set of linear single-view learning problems by bridging our problem to an easily solvable approach. Compared to the conventional methods which learn the entire projection matrix, our algorithm independently optimizes each column of the projection matrix for each class, which can be easily parallelized. In each column optimization, the most computationally intensive step is pure and simple matrix-by-vector multiplication. As a result, our algorithm is much more applicable to large-scale problems than the multiview learning methods with a nonlinear computational cost. Moreover, rigorous convergence proof of the proposed algorithm is also provided. To evaluate the effectiveness of the proposed approach, experimental comparisons are made with state-of-the-art algorithms in multiclass and multilabel classification tasks on many multiview benchmarks. We also report the efficiency comparison results on different numbers of data samples. The experimental results demonstrate that our algorithm can achieve superior performance to all the compared algorithms.

Learning multiple heterogeneous features from different data sources is challenging. One research topic is how to exploit and utilize the correlations among various features across multiple views with the aim of improving the performance of learning tasks, such as classification. In this paper, we propose a new multi-view feature learning algorithm that simultaneously analyzes features from different views. Compared to most of the existing subspace learning methods that only focus on exploiting a shared latent subspace, our algorithm not only learns individual information in each view but also captures feature correlations among multiple views by learning a shared component. By assuming that such a component is shared by all views, we simultaneously exploit the shared component and individual information of each view in a batch mode. Since the objective function is non-smooth and difficult to solve, we propose an efficient iterative algorithm for optimization with guaranteed convergence. Extensive experiments are conducted on several benchmark datasets. The results demonstrate that our proposed algorithm performs better than all the compared multi-view learning algorithms.

We present a new method to efficiently generate a set of morphologically diverse and inspiring virtual trees through hierarchical topology-preserving blending, aiming to facilitate designers' creativity production. By maintaining the topological consistency of the tree branches, sequences of similar yet different trees and novel intermediate trees with encouragingly interesting structures are generated by performing inner-species and cross-species blending, respectively. Hierarchical fuzzy correspondences are automatically established between two or multiple trees based on the multi-scale topology tree representations. Fundamental blending tasks including morph, grow and wilt are introduced and organized into a tree-structured blending scheduler, which not only introduces the randomness into the blending procedure but also wisely schedules the tasks to generate topology-aware blending sequences, contributing to a variety of resulting trees that exhibit diversities in both geometry and topology. Most significantly, multiple batches of blending can be executed in parallel, resulting in a rapid creation of a large repository of diverse trees.

Sexual reproduction plays an important role in evolution. However, in classic genetic algorithms(GAs), the evolutionary process is only implemented on an unisexual population. Although some sexual selection schemes for GAs have been proposed, only limited studies are focused on detailed mechanisms in sexual selection. In this paper, we focus on the modeling of some significant components in sexual selection, including the concepts of male trait, female mating preference. Thereafter, a novel evolutionary framework is constructed based on these models. The theoretical principle of this framework is the famous mechanism called Fisher's runaway process. Numeric optimization is carried out to evaluate the newly proposed framework on a large number of benchmark functions used in CEC2005 Special Session. Comparing with a classic real-coded genetic algorithm, the novel framework outperforms it on most functions. Although this framework is very preliminary, it has shown good potential in solving optimization problems.