Classifiability-based discriminatory projection pursuit

Multi-View Fusion with Extreme Learning Machine for Clustering

Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC’s clustering accuracy.

Sequential Independent Component Analysis Density Estimation

A problem of multivariate probability density function estimation by exploiting linear independent components analysis (ICA) is addressed. Historically, ICA density estimation was initially proposed under the name projection pursuit density estimation (PPDE) and two basic methods, named forward and backward, were published. We derive a modification of the forward PPDE method, which avoids a computationally demanding optimization involving Monte Carlo sampling of the original method. The results of the experiments show that the proposed method presents an attractive choice for density estimation, which is pronounced for a small number of training observations. Under such conditions, our method usually outperforms model-based Gaussian mixture model. We also found that our method obtained better results than the backward PPDE methods in the situation of nonfactorizable underlying density functions. The proposed method has demonstrated a competitive performance compared with the support vector machine and the extreme learning machine in some real classification tasks.

Exponential local discriminant embedding and its application to face recognition

Local discriminant embedding (LDE) has been recently proposed to overcome some limitations of the global linear discriminant analysis method. In the case of a small training data set, however, LDE cannot directly be applied to high-dimensional data. This case is the so-called small-sample-size (SSS) problem. The classical solution to this problem was applying dimensionality reduction on the raw data (e.g., using principal component analysis). In this paper, we introduce a novel discriminant technique called "exponential LDE" (ELDE). The proposed ELDE can be seen as an extension of LDE framework in two directions. First, the proposed framework overcomes the SSS problem without discarding the discriminant information that was contained in the null space of the locality preserving scatter matrices associated with LDE. Second, the proposed ELDE is equivalent to transforming original data into a new space by distance diffusion mapping (similar to kernel-based nonlinear mapping), and then, LDE is applied in such a new space. As a result of diffusion mapping, the margin between samples belonging to different classes is enlarged, which is helpful in improving classification accuracy. The experiments are conducted on five public face databases: Yale, Extended Yale, PF01, Pose, Illumination, and Expression (PIE), and Facial Recognition Technology (FERET). The results show that the performances of the proposed ELDE are better than those of LDE and many state-of-the-art discriminant analysis techniques.