稀疏表示已被证明是高光谱图像(HSI)分类中的有力工具,同时利用多种特征信息进行联合分类的优点在HSI图像分类领域受到关注,但多特征数据的稀疏策略以及数据的非线性是两个棘手的问题.为此提出了自适应稀疏模式的核联合稀疏模型对高光谱图像进行分类.对于几个互补特征(梯度,文理和形状),该模型同时获取每种特征的表示向量,并且通过施加自适应稀疏策略ladaptive,0来有效利用多特征信息.自适应稀疏策略,不仅限制不同特征空间的像素通过来自特定类的原子表示,而且允许这些像素选定的原子不同,从而提供更好的表示方法.此外,提出的核联合稀疏表示模型用于处理数据的非线性问题.核模型将数据投影到高维空间以提高可分离性,实现比线性模型更好的性能.在数据集Indian Pines和University of Pavia的实验结果表明,所提出的算法表现出更高的分类精度.
高光谱图像在采集过程中极易产生高斯、椒盐、条纹等噪声,从而对后续的地物空间识别工作产生影响.因此有效的噪声去除工作在高光谱图像处理中是不可缺少的一步.鲁棒主成分分析(Robust Principal Component Analysis,RPCA)是能将受稀疏噪声干扰的低秩矩阵进行有效恢复的模型.高光谱图像由于其光谱特征之间存在很高的相关性,即每个光谱特征可以用光谱端元的线性组合来表示,因此高光谱图像具有高度低秩性,从而RPCA算法能在高光谱图像去噪中取得显著的效果.结合高光谱图像空间邻域相似性和改进RPCA(Spatial Neighboring Similarity and Improve RPCA,S_IRPCA),提出一种新的高光谱图像去噪算法.算法在去除噪声的同时,更好的保留了细节信息.实验表明,算法与主流的低秩恢复算法相比,无论在主观视觉上还是在客观评价指标上,都做到了显著提升.
The Collaborative Filtering(CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems(RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor(TTHybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy.
