Source code of the IPDPS '21 paper: "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu, and Guangming Tan. - SuperScientificSoftwareLaboratory/TileSpMV
FE-1 is a simple algorithm based on basic pixel statistics (presented in “Detecting ants using motion-based foreground detection algorithms” section), and 3-term decomposition38 (dented as FE-2) is an algorithm based on low-rank matrix decomposition for foreground detection in videos. We ...
Other hybrid models merged filter and embedded algorithms. For example, a two-step sparselogistic regression modelwas developed (Algamal & Lee, 2019), which comprises the sure independence screening (SIS) algorithm and the adaptive least absolute shrinkage and selection algorithm (LASSO). First, SIS...
turning portions of the sparse matrix into dense blocks and invoking high-performance BLAS/lapack libraries. It is designed with optimization libraries for Levenberg-Marquardt in mind, and aims at reducing part of the complexity offering the best tool for the job. Compared to the library currently...
Their method does not require the reconstruction of the entire matrix. As the authors concluded, their scheme is the first algorithm for recommendation systems that runs in time polylogarithmic in the dimensions of the matrix and provides an example of a quantum machine learning algorithm for a ...
While computational approaches exist to infer cell phase directly from single-cell RNA-sequencing data, existing methods yield poor circadian phase estimates, and do not quantify estimation uncertainty, which is essential for interpretation of results from very sparse single-cell RNA-sequencing data. To...
3、训练集和测试集的输入支持dense matrix 和 sparse matrix,其中sparse matrix采用CSR表示法; 4、对于不平衡数据的处理一般来说从三个方面入手: 1)、对正例和负例赋予不同的C值,例如正例远少于负例,则正例的C值取得较大,这种方法的缺点是可能会偏离原始数据的概率分布; ...
matrix. Furthermore, we apply the proposed algorithm to sparse systems arising from discretizations of the one-dimensional heat equation and the two-dimensional Poisson’s equation. Numerical simulations illustrate the capability and effectiveness of the proposed algorithm comparing to well-known methods ...
2010) first analyzes the user-item matrix to identify relationships between different items and then use these relationships to indirectly compute recommendations for users. However, there are some problems, such as data sparsity, cold start and poor scalability. User-based collaborative filtering (...
where S is the covariance matrix of the dataset. Pearson correlation: Pearson correlation has found great application in clustering gene expression data. Pearson correlation evaluates the similarities in the shape of gene expression pattern in this case (Xu and Wunsch, 2005). The Pearson correlation...