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BACKGROUND Jatropha curcas is recognized as a new energy crop due to the presence of the high amount of oil in its seeds that can be converted into biodiesel. The quality and performance of the biodiesel depends on the chemical composition of the fatty acids present in the oil. The fatty acids profile of the oil has a direct impact on ignition quality, heat(More)
Most of the world's natural fiber comes from cotton (Gossypium spp.), which is an important crop worldwide. Characterizing genes that regulate cotton yield and fiber quality is expected to benefit the sustainable production of natural fiber. Although a huge number of expressed sequence tag sequences are now available in the public database, large-scale gene(More)
Manifold learning is an effective feature extraction technique, which seeks a low-dimensional space where the manifold structure, in terms of local neighborhood, of the data set can be well preserved. A typical manifold learning method constructs a local neighborhood centered at individual samples. In this paper, we propose to construct local neighborhoods(More)
research topic in the field of biometrics, since very limited training samples and image discriminant information can be acquired. We propose a new multi-modal biometrics fusion approach to try to solve this problem, which uses face and palmprint biometrics. We combine the normalized Gaborface and Gaborpalm images in the pixel level, and present a Kernel(More)
For nonlinear discrimination analysis technique, there are some key points worthy of further research. One is finding an effective rule to select appropriate kernel function parameter for different sample sets. Another is providing a simple and efficient solution for the singularity problem of within-class scatter matrix. In this paper, we focus on these(More)
In the past few years, manifold learning and sparse representation have been widely used for feature extraction and dimensionality reduction. The sparse representation technique shows that one sample can be linearly recovered by the others in a data set. Based on this, sparsity preserving projections (SPP) has recently been proposed, which simply minimizes(More)
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