# sparse coding

(1.5 hours to learn)

## Summary

Sparse coding is a probabilistic model of natural images where each region of an image is represented as a linaer combination of a small number of components drawn from a dictionary. When the model is fit to natural images, the dictionary elements resemble the receptive fields of cells in the primary visual cortex.

## Context

This concept has the prerequisites:

- maximum likelihood
- matrix multiplication (Sparse coding is a kind of matrix factorization.)
- heavy-tailed distributions (Sparse coding uses heavy-tailed distributions for the coefficients.)
- optimization problems (Fitting sparse coding requires solving an optimization problem.)

## Core resources (read/watch one of the following)

## -Free-

→ Emergence of simple-cell receptive field properties by learning a sparse code for natural images

## -Paid-

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 13.8, pages 468-474

## See also

-No Additional Notes-