In this work, we propose a new method, sparse component analysis (SCA), to estimate multiple PCs that are column sparse. The column sparsity is achieved by ...

Jul 1, 2020 · We propose a method that presumes the p \times k matrix becomes approximately sparse after a k \times k rotation. The simplest version of the ...

Aug 4, 2023 · First, sparse. PCA often explains far less variance in the data than PCA does (Figure 1). While this may appear to be a trade-off for sparsity, ...

May 21, 2020 · Spectral clustering is a popular and effective method. • It is essentially principal component analysis (PCA) + clustering. • Can we get ...

Jul 1, 2020 · For this, we propose a new method for sparse PCA. In the simplest version of the algorithm, the component scores and loadings are initialized ...

Through three applications—sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of social networks, ...

Jun 5, 2024 · Chen and Role [25] presented a novel basis for sparse PCA, which approximately transforms an eigenbasis matrix into a sparse matrix by rotation.

Dec 22, 2015 · Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, ...

The algorithm of SPCArt consists of three alternating steps: rotate PCA basis, truncate small entries, and update the rotation matrix. Its performance bounds ...

In this paper, we propose a new sparse principal component analysis method by introducing a new norm to replace the usual norm in traditional eigenvalue ...