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AI · November 28, 2014

IT

Implementing Truncated Matrix Decompositions for Core.Matrix

Eigendecompositions and Singular Value Decompositions appear in a variety of settings in machine learning and data mining. The eigendecomposition looks like so: $$ \mathbf{A}=\mathbf{Q}\mathbf{\Lambda}\mathbf{Q}^{-1} $$ $ \mathbf{Q} $ contains the eigen...

Implementing Truncated Matrix Decompositions for Core.Matrix launched on November 28, 2014 with 12 votes and more than 75% of that year's launches. The site was still online in September 2026, 11 years on.

In plain words

This project implements truncated matrix decomposition algorithms for Core.Matrix, a Clojure linear algebra library. It provides efficient eigendecomposition and singular value decomposition capabilities for machine learning and data mining applications. The truncated variants allow users to compute only a subset of eigenvalues, eigenvectors, singular values, and singular vectors rather than complete decompositions, making computations faster and more memory-efficient for large matrices.

written from the facts on this page · September 2026

From the sources

Eigendecompositions and Singular Value Decompositions appear in a variety of settings in machine learning and data mining. The eigendecomposition looks like so: $ \mathbf{Q} $ contains the eigenvectors of $ \mathbf{A} $ and $ \mathbf{\Lambda} $ is a diagonal matrix containing the eigenvalues. $ \mathbf{U} $ contains the eigenvectors of the covariance matrix $ \mathbf{A}\mathbf{A^T} $. $ \mathbf{V} $ contains the eigenvectors of the gram matrix $ \mathbf{A^T}\mathbf{A} $. The truncated variants of these decompositions allow us to compute only a few eigenvalues(vectors) or singular values (vectors). This is important since (i) a lot of times, the smaller eigenvalues are discarded, and (ii)…from blog.shriphani.com

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