论文标题
无监督的选择性歧管矩阵分解
Unsupervised Selective Manifold Regularized Matrix Factorization
论文作者
论文摘要
矩阵分解的歧管正则化方法取决于群集假设，从而在分解空间中保留了输入空间中数据的邻域结构。我们认为，将所有数据点的K-Nodighborhoods用作正规化约束可能会对分解的质量产生负面影响，并提出一种无监督和选择性的正则矩阵分解算法来解决此问题。我们的方法共同学习了一组稀疏的代表及其邻居亲和力，以及数据分解。我们通过放松数据的选择性约束，进一步提出了方法的快速近似。我们提出的算法与基线和最新的歧管正则化和聚类算法具有竞争力。
Manifold regularization methods for matrix factorization rely on the cluster assumption, whereby the neighborhood structure of data in the input space is preserved in the factorization space. We argue that using the k-neighborhoods of all data points as regularization constraints can negatively affect the quality of the factorization, and propose an unsupervised and selective regularized matrix factorization algorithm to tackle this problem. Our approach jointly learns a sparse set of representatives and their neighbor affinities, and the data factorization. We further propose a fast approximation of our approach by relaxing the selectivity constraints on the data. Our proposed algorithms are competitive against baselines and state-of-the-art manifold regularization and clustering algorithms.
