Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into ...

Abstract: Boolean matrix factorization method can be used to create an approximate circuit from a given circuit. The accuracy loss of approximate circuit originates ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

In this lesson, we will look at another matrix factorization technique called Alternating Least Squares (ALS). This method can prove to be much more effective and robust than the SVD we saw earlier.

Abstract: In this paper, we propose a new low-rank matrix factorization model, dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix X and a factorization rank r, BSSMF ...

help?>qrfact qrfact(A) -> QRSparse Compute the QR factorization of a sparse matrix A. Fill-reducing row and column permutations are used such that F.R = F.Q'*A[F.prow,F.pcol]. The main application of ...

Matrix decomposition is an area of linear algebra which is focused on expressing a matrix as a product of matrices with prescribed properties. (Photo credit: Merino et al., 2024) Imagine discovering ...

Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), Vol. 38, No. 4 (Oct., 1976), pp. 400-403 (4 pages) A theorem involving the decomposition of an idempotent matrix into the sum of one or ...

Inverting a matrix is one of the most common tasks in data science and machine learning. In this article I explain why inverting a matrix is very difficult and present code that you can use as-is, or ...