ArticleJournal of applied statistics2026
Cardinality-based sparse singular value decomposition for similarity matrices.
Article in Journal of applied statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sparse decomposition methods have been studied in the context of principal component analysis (PCA). Many of these methods control the number of non-zero elements of the eigenvector through the tuning of a regularization parameter(s). Other approaches allow for the direct choice of cardinality to create sparse eigenvectors. As PCA is not applicable to all settings, such as analyzing the cross-correlation matrix, we extend cardinality-based PCA to cardinality-based singular value decomposition (SVD). Our method allows the user to independently input their desired cardinality of the left and right singular vectors of any continuous data matrix. This will create sparse singular vectors consisting of the most impactful variables. In addition, we extend our method from a rank-1 SVD approximation to an SVD approximation greater than rank 1, and create left and right matrices of sparse singular vectors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.