Sparse representation in machine learning refers to a way of representing data or models where most of the elements are zero or near-zero, leading to a high degree of sparsity. This is particularly useful in scenarios where data naturally contains many zeroes or in situations where a more compact representation is beneficial.
How Sparse Representation Works
Consider a natural language processing task where a large vocabulary is used, but each document only contains a small subset of the words. Representing each document as a vector, most elements would be zero, indicating the absence of most words. This sparsity can be exploited for more efficient storage and faster computations.
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