Search

Graph-based technique is widely used in projection, clustering, and classification tasks. In this article, we propose a novel and solid framework, named unsupervised projection with graph optimization (UPGO), for both dimensionality reduction and clustering. Different from the existing algorithms which treat graph construction and projection learning as two separate steps, UPGO unifies graph construction and projection learning into a general framework. It learns the graph similarity matrix adaptively based on the relationships among the low-dimensional representations. A constraint is introduced to the Laplacian matrix to learn a structured graph which contains the clustering structure, from which the clustering results can be obtained directly without requiring any postprocessing. The structured graph achieves the ideal neighbors assignment, based on which an optimal low-dimensional subspace can be learned. Moreover, we generalize UPGO to tackle the semisupervised case, namely semisupervised projection with graph optimization (SPGO), a framework for both dimensionality reduction and classification. An efficient algorithm is derived to optimize the proposed frameworks. We provide theoretical analysis about convergence analysis, computational complexity, and parameter determination. Experimental results on real-world data sets show the effectiveness of the proposed frameworks compared with the state-of-the-art algorithms. Results also confirm the generality of the proposed frameworks. [ABSTRACT FROM AUTHOR]

Unsupervised feature selection is fundamentally important for processing unlabeled high-dimensional data, and several methods have been proposed on this topic. Most existing embedded unsupervised methods just emphasize the data structure in the input space, which may contain large noise. Therefore, they are limited to perceive the discriminative information implied within the low-dimensional manifold. In addition, these methods always involve several parameters to be tuned, which is time-consuming. In this paper, we present a self-tuned discrimination-aware (STDA) approach for unsupervised feature selection. The main contributions of this paper are threefold: 1) it adopts the advantage of discriminant analysis technique to select the valuable features; 2) it learns the local data structure adaptively in the discriminative subspace to alleviate the effect of data noise; and 3) it performs feature selection and clustering simultaneously with an efficient optimization strategy, and saves the additional efforts to tune parameters. Experimental results on a toy data set and various real-world benchmarks justify the effectiveness of STDA on both feature selection and data clustering, and demonstrate its promising performance against the state of the arts. [ABSTRACT FROM AUTHOR]

In image classification, feature combination is often used to combine the merits of multiple complementary features and improve the classification accuracy compared with one single feature. Existing feature combination algorithms, e.g., multiple kernel learning, usually determine the weights of features based on the optimization with respect to some classifier-dependent objective function. These algorithms are often computationally expensive, and in some cases are found to perform no better than simple baselines. In this paper, we solve the feature combination problem from a totally different perspective. Our algorithm is based on the simple idea of combining only base kernels suitable to be combined. Since the very aim of feature combination is to obtain the highest possible classification accuracy, we measure the combination suitableness of two base kernels by the maximum possible cross-validation accuracy of their combined kernel. By regarding the pairwise suitableness as the kernel adjacency, we obtain a weighted graph of all base kernels and find that the base kernels suitable to be combined correspond to a cluster in the graph. We then use the dominant sets algorithm to find the cluster and determine the weights of base kernels automatically. In this way, we transform the kernel combination problem into a clustering one. Our algorithm can be implemented in parallel easily and the running time can be adjusted based on available memory to a large extent. In experiments on several data sets, our algorithm generates comparable classification accuracy with the state of the art. [ABSTRACT FROM AUTHOR]