Although linear principal component analysis (PCA) originates from the work of Sylvester [67] and Pearson [51], the development of nonlinear counterparts has only received attention from the 1980s. Work on nonlinear PCA, or NLPCA, can be divided into the utilization of autoassociative neural networks, principal curves and manifolds, kernel approaches or the combination of these approaches. This article reviews existing algorithmic work, shows how a given data set can be examined to determine whether a conceptually more demanding NLPCA model is required and lists developments of NLPCA algorithms. Finally, the paper outlines problem areas and challenges that require future work to mature the NLPCA research field.

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Developments and Applications of Nonlinear Principal Component Analysis – a Review

The rapid deployment of lithium-ion batteries in clean energy and electric vehicle applications will also increase the volume of retired batteries in the coming years. Retired Li-ion batteries could have residual capacities up to 70–80% of the nominal capacity of a new battery, which could be lucrative for a second-life battery market, also creating environmental and economic benefits. Presently, retired batteries are first screened to select usable batteries and then a proper secondary application is choosen according to the battery performance. Here, a complete process for grouping used batteries is proposed including safety checking, performance evaluation, data processing, and clustering of batteries. Also, a novel clustering algorithm of retired batteries based on traversal optimization is proposed. The new method does not require defining the cluster numbers and centers in beforehand, but possesses immunity to outliers. It can be used both for small and large sample sizes, as the optimization parameters used do not require iteration. The Davies-Bouldin Index of the proposed algorithm shows that the greatest differences are found between clusters, but the least differences between the samples within a single cluster, which indicates the effectiveness of the algorithm.

A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries

With the large-scale proliferation of biometric systems, privacy and irrevocability issues of their data have become a hot research issue. Recently, some researchers proposed a couple of schemes to generate BioHash, e.g. PalmHash, however, in this paper, we point out that the previous schemes are costly in terms of computational complexity and possession of USB tokens to generate pseudorandom numbers. To overcome the problems, we propose a novel chaotic FaceHashing scheme which preserves privacy of a biometric user. The presented scheme does not need users to posses USB tokens in generating pseudorandom sequences, which is a cost-effective solution. Besides, our scheme minimizes the system complexity with simple operations to attain the FaceHash. Experimental results show that the proposed scheme is efficient, secure, and revocable in case FaceHash is theft or compromised.

Privacy-preserving and tokenless chaotic revocable face authentication scheme

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Mhs2002 proceedings of 2002 international symposium on micromechatronics and human science

Illumination Compensation for Face Recognition Using Only One Image

At the general recognition process, the feature vectors that are obtained from some facial images are transformed into recognition space by Fisher's linear discriminate method (Fisher's method) and principal component analysis (PCA). But at Fisher's method we must recalculate all recognition space when adding a registrant or registrant's learning patterns. In contrast, though at PCA we only recalculate added registrant's pace when adding, the face recognition rate obtained from the conventional PCA is bad, because the aim of the conventional PCA is dimension curtailment for compression of data and isn't dimension curtailment for recognition. Therefore we proposed improved principal component analysis (IPCA) for pattern recognition.

Jianming Yang

Papers

Face recognition using improved principal component analysis