Trends in extreme learning machines

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A Course In Functional Analysis

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Ensemble learning for data stream analysis

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Semi-Supervised and Unsupervised Extreme Learning Machines

Fuzziness based semi-supervised learning approach for intrusion detection system

Online sequential extreme learning machine with forgetting mechanism

Finger vein biometric has received considerable attentions in recent years. However, there is no approved and benchmark finger vein database for researchers to evaluate their algorithms. Furthermore, few public finger vein databases are available online. Aiming to support a benchmark database, in this paper, we introduce a representative finger vein database captured by a portable device, which is named MMCBNU_6000. Our research is novel in four aspects. First, MMCBNU_6000 is established with participation of 100 volunteers, coming from 20 countries. It contains images acquired from different persons with different skin colors. Second, statistical information of the nationality, age, gender, and blood type is recorded for further analysis on finger vein images. Third, similar to the real application, influences from translation, rotation, scale, uneven illumination, scattering, collection posture, finger tissue and finger pressure are taken into account in the imaging process. Fourth, according to the evaluation of average image gray value, image contrast and entropy on the images from the available databases, the acquired images in MMCBNU_6000 have comparable image quality.

An available database for the research of finger vein recognition

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

Finger Vein Recognition Using Generalized Local Line Binary Pattern

Finger vein has been proved to be an effective biometric for personal identification in recent years. Inspired by the good power of Gabor filter in capturing specific texture characteristics from any orientation of an image, this paper proposes a simple, yet powerful and efficient local descriptor for finger vein recognition, called histogram of competitive Gabor responses (HCGR). Specially, HCGR is based on a set of competitive Gabor response (CGR) which consists of two components: competitive Gabor magnitude (CGM) and competitive Gabor orientation (CGO). A set of CGR includes the information on magnitude and orientation of the maximum responses of the Gabor filter bank with a number of different orientations. For a given image, we calculate its CGM image and CGO image and represent them in a concatenated histogram, called HCGR. This histogram can efficiently and effectively exploit the discriminative orientation and local features in a finger vein image. The experimental results obtained on our publically available finger vein image database MMCBNU_6000 demonstrate that the proposed HCGR outperforms the classical local operators such as Gabor, steerable, histogram of oriented gradients (HOG) and local binary pattern (LBP).

Finger Vein Recognition Using Histogram of Competitive Gabor Responses

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.

/pdf/a-high-accuracy-pedestrian-detection-system-combining-a-4qy6wv22xd.pdf

Zhihui Wang

Papers

Online sequential extreme learning machine with forgetting mechanism

An available database for the research of finger vein recognition

Finger Vein Recognition Using Generalized Local Line Binary Pattern

Finger Vein Recognition Using Histogram of Competitive Gabor Responses

A high accuracy pedestrian detection system combining a cascade AdaBoost detector and random vector functional-link net.