Neural Networks Hebbian
I recently joined a new open source project called GHAPACK. The project currently provides the functionality and the means to use the Generalized Hebbian Algorithm. I came across this project after banging my head against some of the practical limitations of Singular Value Decomposition (SVD). GHA is a Hebbian-based neural network-like algorithm that approximates SVD’s ability to perform eigen decomposition. Its added bonus is that it allows for incremental training so you can refine your model with new data without having to recompute using the entire dataset.
Your Trusty SVD Tool
SVD is one of those tools that every machine learning practitioner and computational geek will pull out at some time or another. It’s a powerful matrix factorization technique that allows you to get at the matrix’s eigenvectors and eigenvalues. One of reason it tends to be used so often is the fact that it can be used on those pesky M x N matrices, which us data junkies tend to generate.
For most small problems I can just use scipy and numpy’s svd and never give it a second thought. LAPACK’s suite of SVD routines power the svd functions of scipy, numpy, and MATLAB among others. It is developed for dense matrices and processes them in their entirety. What happens when you start dealing with problems in high-dimensional space? Those dense representations and full processing are expensive. So, when your problem space is better suited for sparse matrices you tend to run into not enough memory, non-convergence…no SVD.
Source: Kurt GrandisKurt Grandis
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