提取有效特征对高维数据的模式分类起着关键作用.零空间线性判别分析(null-space linear discriminant analysis,NLDA)在数据降维和特征提取上表现出较好的性能,但是该方法本质上仍是一种线性方法.为有效提取数据的非线性特征,提出了零...提取有效特征对高维数据的模式分类起着关键作用.零空间线性判别分析(null-space linear discriminant analysis,NLDA)在数据降维和特征提取上表现出较好的性能,但是该方法本质上仍是一种线性方法.为有效提取数据的非线性特征,提出了零空间核判别分析算法(null-space kernel discriminant analysis,NKDA)并将其应用于人脸识别.利用核函数将原始样本隐式地映射到高维特征空间后,采用一次瘦QR分解求核类内散布矩阵的零空间鉴别矢量集,最后再进行一次Cholesky分解求得具正交性的核空间鉴别矢量集.与NLDA相比,NKDA具有更好的识别性能且在大样本情况下也能应用.另外,基于NKDA,提出了增量NKDA算法,当增加新的训练样本时能正确地更新NKDA鉴别矢量集.在ORL库、Yale库和PIE子库上的实验结果表明了算法的有效性和效率,在有效降维的同时能进一步提高鉴别能力.展开更多
The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more th...The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.展开更多
基金Supported by the National Key Research and Development Program of China(2017YFA0604300)National Natural Science Foundation of China(51779278,51379224,and 41671398)NOAA/CPO Modeling,Analyses,Predictions,and Projections(MAP) Program
文摘The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.
