# Linear discriminant analysis and Bayes rule: classification

What is the relation between Linear discriminant analysis and Bayes rule? I understand that LDA is used in classification by trying to minimize the ratio of within group variance and between group variance, but I don't know how Bayes rule use in it.

• Discriminant functions are extracted so as to maximize between-group variation to within-group variation ratio. It has nothing to do with classification, which is the second and stand-alone stage of LDA. – ttnphns Jun 29 '12 at 18:20

Classification in LDA goes as follows (Bayes' rule approach). [About extraction of discriminants one might look here.]

According to Bayes theorem, the sought-for probability that we're dealing with class $k$ while observing currently point $x$ is $P(k|x) = P(k)*P(x|k) / P(x)$, where

$P(k)$ – unconditional (background) probability of class $k$; $P(x)$ – unconditional (background) probability of point $x$; $P(x|k)$ – probability of presence of point $x$ in class $k$, if class being dealed with is $k$.

"Observing currently point $x$" being the base condition, $P(x)=1$, and so the denominator can be omitted. Thus, $P(k|x) = P(k)*P(x|k)$.

$P(k)$ is a prior (pre-analytical) probability that the native class for $x$ is $k$; $P(k)$ is specified by user. Usually by default all classes receive equal $P(k)$ = 1/number_of_classes. In order to compute $P(k|x)$, i.e. posterior (post-analytical) probability that the native class for $x$ is $k$, one should know $P(x|k)$.

$P(x|k)$ - probability per se - can't be found, for discriminants, the main issue of LDA, are continuous, not discrete, variables. Quantity expressing $P(x|k)$ in this case and proportional to it is the probability density (PDF function). Hereby we need to compute PDF for point $x$ in class $k$, $PDF(x|k)$, in $p$-dimensional normal distribution formed by values of $p$ discriminants. [See Wikipedia Multivariate normal distribution]

$$PDF(x|k) = \frac {e^{-d/2}} {(2\pi)^{p/2}\sqrt{\bf |S|})}$$

where $d$ – squared Mahalanobis distance [See Wikipedia Mahalanobis distance] in the discriminants' space from point $x$ to a class centroid; $\bf S$ – covariance matrix between the discriminants, observed within that class.

Compute this way $PDF(x|k)$ for each of the classes. $P(k)*PDF(x|k)$ for point $x$ and class $k$ express the sought-for $P(k)*P(x|k)$ for us. But with the above reserve that PDF isn't probability per se, only proportional to it, we should normalize $P(k)*PDF(x|k)$, dividing by the sum of $P(k)*PDF(x|k)$s over all classes. For example, if there are 3 classes in all, $k$, $l$, $m$, then

$P(k|x) = P(k)*PDF(x|k) / [P(k)*PDF(x|k)+P(l)*PDF(x|l)+P(m)*PDF(x|m)]$

Point $x$ is assigned by LDA to the class for which $P(k|x)$ is the highest.

Note. This was the general approach. Many LDA programs by default use pooled within-class matrix $\bf S$ for all classes in the formula for PDF above. If so, the formula simplifies greatly because such $\bf S$ in LDA is identity matrix (see the bottom footnote here), and hence $\bf |S|=1$ and $d$ turns into squared euclidean distance (reminder: the pooled within-class $\bf S$ we are talking about is covariances between the discriminants, - not between the input variables, which matrix is usually designated as $\bf S_w$).

Addition. Before the above Bayes rule approach to classification was introduced to LDA, Fisher, LDA pioneer, proposed computing the now so called Fisher's linear classification functions to classify points in LDA. For point $x$ the function score of belonging to class $k$ is linear combination $b_{kv1}V1_x+b_{kv2}V2_x+...+Const_k$, where $V1, V2,...V_p$ are the predictor variables in the analysis.

Coefficient $b_{kv}=(n-g)\sum_w^p{s_{vw}\bar{V}_{kw}}$, $g$ being the number of classes and $s_{vw}$ being the element of the pooled within-class scatter matrix of $p$ $V$-variables.

$Const_k=\log(P(k))-(\sum_v^p{b_{kv}\bar{V}_{kv}})/2$.

Point $x$ gets assigned to the class for which its score is the highest. Classification results obtained by this Fisher's method (which bypasses extraction of discriminants engaged in the complex eigendecomposition) are identical with those obtained by Bayes' method only if pooled within-class covariance matrix is used with Bayes' method based on discriminants (see "Note" above) and all the discriminants are being used in the classification. The Bayes' method is more general because it allows using separate within-class matrices as well.

• This is Bayesian approach right? What is the Fisher's approach for this? – zca0 Jul 19 '12 at 14:36
• Added to the answer upon your request – ttnphns Jul 20 '12 at 8:34
• +1 for distinguishing between Bayes' and Fisher's approach of LDA. I'm a new-comer in LDA, and the books I read teach me LDA in Bayes' approach, which classifies $X$ to class $K$ with the highest $p(K|X)$, so I have to compute all the $p(K|X)$ for each class $K$, right? By Fisher's approach, I just need to figure out the discriminants and their corresponding coefs, and no need to compute the posterior for each class, right? – avocado Feb 23 '14 at 3:53
• And I think the Bayes' approach is more understandable, and why do we need to use the Fisher's approach? – avocado Feb 23 '14 at 6:01
• We don't need. Just for historical matter. – ttnphns Feb 23 '14 at 6:40

Assume equal weights for the two error types in a two class problem. Suppose the two classes have a multivariate class conditional density of the classification variables. Then for any observed vector $x$ and class conditional densities $f_1(x)$ and $f_2(x)$ the Bayes rule will classify $x$ as belonging to group 1 if $f_1(x) \geq f_2(x)$ and as class 2 otherwise. The Bayes rule turns out to be a linear discriminant classifier if $f_1$ and $f_2$ are both multivariate normal densities with the same covariance matrix. Of course in order to be able to usefully discriminate the mean vectors must be different. A nice presentation of this can be found in Duda and Hart Pattern Classification and Scene Analysis 1973 (the book has recently been revised but I like particularly the presentation in the original edition).