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中文摘要：
为了更加准确地对图像进行聚类与分类,提出一种基于局部样条嵌入的正交半监督子空间学习算法.通过学习一个正交投影矩阵,使得训练样本中的标注数据经过投影矩阵降维后类间离散度尽量大,类内离散度尽量小;采用局部样条回归将局部低维嵌入坐标映射成全局低维嵌入坐标,使得被投影数据保持原有流形结构,并有效地利用有标注训练样本和未标注训练样本得到优化的图像表达方式.图像聚类与分类实验的结果表明了文中算法的有效性.
英文摘要：
In order to improve the performance of image clustering and classification,this paper proposes a semi-supervised orthogonal projection with spline embedding（SOPSE）.SOPSE utilizes both labeled and unlabeled samples to learn an orthogonal projection subspace where the separability between different classes is maximized and the separability within the same classes is minimized.At the same time,SOPSE can guarantee the manifold geometry of original high-dimensional data by transforming local coordinators to global coordinators in reduced subspace with local spline embedding.The experiments demonstrate the effectiveness of the proposed method.
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Personalized retrieval of sports video based on multi-modal analysis and user preference acquisition
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