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Multicollinearity and Partial Least Squaressingular (determinant equal to zero) correlation matrix. However, even when we do not reach this extreme, multicollinearity causes problems: because of the redundancy in the variables, we may have an ill-conditioned correlation matrix which is very prone to large variances which may be due to noise in the data set.
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Hebbian Learning and Negative Feedback Networks978-1-84628-118-1Series ISSN 1610-3947 Series E-ISSN 2197-8441