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Feature Extraction via Sparse Difference Embedding (SDE)
( Minghua Wan ),( Zhihui Lai ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.7
The traditional feature extraction methods such as principal component analysis (PCA) cannot obtain the local structure of the samples, and locally linear embedding (LLE) cannot obtain the global structure of the samples. However, a common drawback of existing PCA and LLE algorithm is that they cannot deal well with the sparse problem of the samples. Therefore, by integrating the globality of PCA and the locality of LLE with a sparse constraint, we developed an improved and unsupervised difference algorithm called Sparse Difference Embedding (SDE), for dimensionality reduction of high-dimensional data in small sample size problems. Significantly differing from the existing PCA and LLE algorithms, SDE seeks to find a set of perfect projections that can not only impact the locality of intraclass and maximize the globality of interclass, but can also simultaneously use the Lasso regression to obtain a sparse transformation matrix. This characteristic makes SDE more intuitive and more powerful than PCA and LLE. At last, the proposed algorithm was estimated through experiments using the Yale and AR face image databases and the USPS handwriting digital databases. The experimental results show that SDE outperforms PCA LLE and UDP attributed to its sparse discriminating characteristics, which also indicates that the SDE is an effective method for face recognition.
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Zixun Xiong,Minghua Wan,Rui Xue,Guowei Yang 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.9
Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It’s able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don’t always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.
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Manganese-cobalt hexacyanoferrate cathodes for sodium-ion batteries
Pasta, Mauro,Wang, Richard Y.,Ruffo, Riccardo,Qiao, Ruimin,Lee, Hyun-Wook,Shyam, Badri,Guo, Minghua,Wang, Yayu,Wray, L. Andrew,Yang, Wanli,Toney, Michael F.,Cui, Yi The Royal Society of Chemistry 2016 Journal of materials chemistry. A, Materials for e Vol.4 No.11
<P>Prussian Blue analogues (PBAs) have shown promise as electrode materials for grid-scale batteries because of their high cycle life and rapid kinetics in aqueous-based electrolytes. However, these materials suffer from relatively low specific capacity, which may limit their practical applications. Here, we investigate strategies to improve the specific capacity of these materials while maintaining their cycling stability and elucidate mechanisms that enhance their electrochemical properties. In particular, we have studied the electrochemical and structural properties of manganese hexacyanoferrate (MnHCFe) and cobalt hexacyanoferrate (CoHCFe) in an aqueous, sodium-ion electrolyte. We also studied manganese-cobalt hexacyanoferrate (Mn-CoHCFe) solid solutions with different Mn/Co ratios that combine properties of both MnHCFe and CoHCFe. The materials have the characteristic open-framework crystal structure of PBAs, and their specific capacities can be significantly improved by electrochemically cycling (oxidizing and reducing) both the carbon-coordinated Fe and the nitrogen-coordinated Co or Mn ions.<I>In situ</I>synchrotron X-ray diffraction studies and<I>ex situ</I>soft X-ray absorption spectroscopy combined with an in-depth electrochemical characterization provide insight into the different electrochemical properties associated with the Fe, Co, and Mn redox couples. We show that cycling the C-coordinated Fe preserves the crystal structure and enables the outstanding kinetics and cycle life previously displayed by PBAs in aqueous electrolytes. On the other hand, the N-coordinated Co and Mn ions exhibit a slower kinetic regime due to structural distortions resulting from the weak N-coordinated crystal field, but they still contribute significantly towards increasing the specific capacity of the materials. These results provide the understanding needed to drive future development of PBAs for grid-scale applications that require extremely high cycle life and kinetics.</P>
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