Backer. A maximum variance cluster algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(9):1273-1280, 2002. 5A maximum variance cluster algorithm. Veenman, Cor J.,Reinders, Marcel J.T.,
A maximum variance cluster algorithm IEEE Trans. Pattern Anal. Mach. Intell., 24 (9) (2002), pp. 1273-1280 View in ScopusGoogle Scholar [30] A. Gionis, H. Mannila, P. Tsaparas Clustering aggregation Proceedings of the 21st International Conference on Data Engineering (ICDE'05) (2007) ...
Transfer ‘anchors’ between our data set and the reference published set were determined using the algorithm implemented in Seurat, and we classified our cells into the 24 clusters previously identified. Cluster identities were confirmed with expression levels of cluster markers, and gene expression ...
By the optimization conclusion, all solutions cluster around the global optimum. In the best solution, both the mean and variance are anticipated to be minimal, while the scale parameter λ is expected to be significant. The pseudo code of single objective EDO shown in Algorithm 1....
Hierarchical cluster analysis by the Ward algorithm was performed to find a suitable number of groups within the population. After the number of clusters was chosen by the hierarchical clustering method, a nonhierarchical k-means cluster analysis was carried out to compare the pattern of clusters wi...
such as their minimum expectation value and variance of intra-cluster similarity. It is an agglomerative algorithm, meaning that it starts with separated objects and progressively joins them together to form clusters. PFClust is heuristic in the sense that it cannot be described in terms of optimis...
We show that the expected value of the least-squares solution across all possible genotype datasets is equal to the true solution when part of the problem has been solved, and that the variance of the solution approaches zero as its size increases. The Least-squares algorithm performs nearly as...
Clustering problem of cells x variant (number of reads supporting each allele), fit a mixture model with the cluster centers represented as the alternate allele fraction for each locus in the cluster. A deterministic annealing variant of the expectation maximization algorithm. Doublet identification ...
3 AI Use Cases (That Are Not a Chatbot) Machine Learning Feature engineering, structuring unstructured data, and lead scoring Shaw Talebi August 21, 2024 7 min read Back To Basics, Part Uno: Linear Regression and Cost Function Data Science ...
Smaller batch sizes introduce more noise into the training algorithm due to sample variance, and this noise can have a regularizing effect. Thus, larger batch sizes can be more prone to overfitting and may require stronger regularization and/or additional regularization techniques. In addition, the ...