【摘要】 传统潜在语义分析模型所得到的主题空间映射矩阵往往比较稠密,不仅存储代价比较高,而且各个主题含义不明确。针对该问题,提出一种新的稀疏主题模型,该模型通过对映射矩阵施加稀疏性约束,使得每个主题只与少数词项关联,来增加主题的可解释性;同时,通过对编码系数矩阵施加低秩约束,使得数据在主题空间中呈现出更好的聚类特性。实验结果表明,基于该模型得到的主题空间更有利于分类,映射矩阵的存储代价更低。更多还原

【Abstract】 The project matrix learned by classic Latent Semantic Analysis is always dense,which leads to high storage cost and unclear semantic for each topic.To tackle this problem,a novel sparse topic model was proposed in this paper.By enforcing the sparsity of project matrix,the new model only selects a small number of relevant words for each topic and hence leads to a clear semantic interpretation.Moreover,by enforcing the low rankness of encoding matrix,data projected in the topic subspace shows a better clustering features.Experimental result show that topic subspace learned by our new topic model is in favor of classification,and significantly reduces the storage cost of project matrix.更多还原