Face recognition with lattice independent component analysis and extreme learning machines

Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis

Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.

Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition

A novel weighted hybrid classifier and a high-order, local normal derivative pattern descriptor is proposed for 3D face recognition. The Local derivative pattern (LDP) captures detailed information, based on the local derivative variation in different directions. The LDP is computed on three normal maps in x, y, and z directions and on different scales. The surface normal captures the orientation of a surface at each point of 3D data. More informative local shape information is extracted using the surface normal, as compared to depth. The nth-order LDP on the surface normal is proposed to encode more detailed features from the (n-1)th-order's local derivative direction variations. An extreme learning machine (ELM) based autoencoder, using a multilayer network structure, is employed to select more discriminant features and provide a faster training speed. A weighted hybrid framework is proposed to handle facial challenges using a combination of the ELM and the sparse representation classifier (SRC). The advantage of speed for the ELM and accuracy for the SRC in a weighted scheme is used to enhance the performance of the recognition system. Experimental results regarding four famous 3D face databases illustrate the generalization and effectiveness of the proposed method in terms of both computational cost and recognition accuracy.

Hyperspectral band selection based on a variable precision neighborhood rough set

Band selection is a well-known approach for reducing dimensionality in hyperspectral images. We propose a band-selection method based on the variable precision neighborhood rough set theory to select informative bands from hyperspectral images. A decision-making information system was established by hyperspectral data derived from soybean samples between 400 and 1000 nm wavelengths. The dependency was used to evaluate band significance. The optimal band subset was selected by a forward greedy search algorithm. After adjusting appropriate threshold values, stable optimized results were obtained. To assess the effectiveness of the proposed band-selection technique, two classification models were constructed. The experimental results showed that admitting inclusion errors could improve classification performance, including band selection and generalization ability.

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions such as schizophrenia. This paper deals with the application of ensembles of Extreme Learning Machines (ELM) to build Computer Aided Diagnosis systems on the basis of features extracted from the activity measures computed over rs-fMRI data. The power of ELM to provide quick but near optimal solutions to the training of Single Layer Feedforward Networks (SLFN) allows extensive exploration of discriminative power of feature spaces in affordable time with off-the-shelf computational resources. Exploration is performed in this paper by an evolutionary search approach that has found functional activity map features allowing to achieve quite successful classification experiments, providing biologically plausible voxel-site localizations.

Is extreme learning machine feasible? A theoretical assessment (part II)

An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural network (FNN) learning system that randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons in the second stage. Therefore, ELM training is essentially a linear learning problem, which significantly reduces the computational burden. Numerous applications show that such a computation burden reduction does not degrade the generalization capability. It has, however, been open that whether this is true in theory. The aim of this paper is to study the theoretical feasibility of ELM by analyzing the pros and cons of ELM. In the previous part of this topic, we pointed out that via appropriately selected activation functions, ELM does not degrade the generalization capability in the sense of expectation. In this paper, we launch the study in a different direction and show that the randomness of ELM also leads to certain negative consequences. On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning. On the other hand, we theoretically justify that there also exist activation functions such that the corresponding ELM degrades the generalization capability. In particular, we prove that the generalization capability of ELM with Gaussian kernel is essentially worse than that of FNN with Gaussian kernel. To facilitate the use of ELM, we also provide a remedy to such a degradation. We find that the well-developed coefficient regularization technique can essentially improve the generalization capability. The obtained results reveal the essential characteristic of ELM in a certain sense and give theoretical guidance concerning how to use ELM.

Is extreme learning machine feasible? A theoretical assessment (part I)

An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have demonstrated the feasibility and high efficiency of ELM-like systems. It has, however, been open if this is true for any general applications. In this two-part paper, we conduct a comprehensive feasibility analysis of ELM. In Part I, we provide an answer to the question by theoretically justifying the following: 1) for some suitable activation functions, such as polynomials, Nadaraya-Watson and sigmoid functions, the ELM-like systems can attain the theoretical generalization bound of the FNNs with all connections adjusted, i.e., they do not degrade the generalization capability of the FNNs even when the connections with and within hidden neurons are randomly fixed; 2) the number of hidden neurons needed for an ELM-like system to achieve the theoretical bound can be estimated; and 3) whenever the activation function is taken as polynomial, the deduced hidden layer output matrix is of full column-rank, therefore the generalized inverse technique can be efficiently applied to yield the solution of an ELM-like system, and, furthermore, for the nonpolynomial case, the Tikhonov regularization can be applied to guarantee the weak regularity while not sacrificing the generalization capability. In Part II, however, we reveal a different aspect of the feasibility of ELM: there also exists some activation functions, which makes the corresponding ELM degrade the generalization capability. The obtained results underlie the feasibility and efficiency of ELM-like systems, and yield various generalizations and improvements of the systems as well.