To break this bottleneck, we carefully build a sparse embedded $k$-means clustering algorithm which requires $\\mathcal{O}(nnz(X))$ ($nnz(X)$ denotes the number of non-zeros in $X$) for fast matrix multiplicatio
3.2.2 K-means clustering Clustering is an unsupervised machine learning procedure that involves the gathering of data. Provided a set of data points clustering can be used to characterize every data type into a specific group. Further, this results in the classification into several groups, wh...
and\(c\)is a constant (usually 1). We introduce the elastic net penalty on the cell representation\({s}_{i}\)to encourage sparsity to facilitate cell clustering. Moreover, the norm constraint is imposed to ensure the same scale of each latent dimension in decomposing cellular variation. Eq...
Sparse component analysis based on an improved ant K-means clustering algorithm for underdetermined blind source separation 2019, Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019 Independent component analysis: An introduction 2018, Applied Computing ...
An ordinary classifier generally can not per- form feature selection automatically, but a kind of sparse penalty coupled with the Hellinger distance metric can be embedded into the classifier to achieve such a task. For convenience, a linear SVM classifier is employed as an example to establish ...
A Bayesian method which utilises the rich structure embedded in the sensing matrix for fast sparse signal recovery signal-processingmatlabbayesian-methodssparse-datasparse-reconstructionstatistical-signal-processingsparse-reconstruction-algorithms UpdatedApr 25, 2018 ...
d Finally, we select K features corresponding to neurons with the highest strength values Full size image 2 Related work 2.1 Feature selection The literature on feature selection shows a variety of approaches that can be divided into three major categories, including filter, wrapper, and embedded ...
Therefore, it is assumed that some nonlinear manifolds of opinions are embedded into the high dimensional input space, and a few nearest neighbors can construct each data point in its manifold. The weights of this reconstruction for all data are learned by imposing the mentioned assumptions as pri...
For object detection and classification, a multi-category generative model may be used based on k-means clustering of sparse C-cell column responses. This model may be trained in a semi-supervised way, allowing the image background to divide up into unlabeled categories (e.g., 30 categories—...
The user computing device 102 can be any type of computing device, such as, for example, a personal computing device (e.g., laptop or desktop), a mobile computing device (e.g., smartphone or tablet), a gaming console or controller, a wearable computing device, an embedded computing devic...