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+---
+title: Discriminant analysis
+date: 2019-01-03
+template: post
+comments: true
+---
+
+In this post I will talk about theory and implementation of linear and
+quadratic discriminant analysis, classical methods in statistical
+learning.
+
+[]{#Acknowledgement}**Acknowledgement**. Various sources were of great
+help to my understanding of the subject, including Chapter 4 of [The
+Elements of Statistical
+Learning](https://web.stanford.edu/~hastie/ElemStatLearn/), [Stanford
+CS229 Lecture notes](http://cs229.stanford.edu/notes/cs229-notes2.pdf),
+and [the scikit-learn
+code](https://github.com/scikit-learn/scikit-learn/blob/7389dba/sklearn/discriminant_analysis.py).
+
+Theory {#Theory}
+------
+
+Quadratic discriminant analysis (QDA) is a classical classification
+algorithm. It assumes that the data is generated by Gaussian
+distributions, where each class has its own mean and covariance.
+
+$$(x | y = i) \sim N(\mu_i, \Sigma_i).$$
+
+It also assumes a categorical class prior:
+
+$$\mathbb P(y = i) = \pi_i$$
+
+The log-likelihood is thus
+
+$$\begin{aligned}
+\log \mathbb P(y = i | x) &= \log \mathbb P(x | y = i) \log \mathbb P(y = i) + C\\
+&= - {1 \over 2} \log \det \Sigma_i - {1 \over 2} (x - \mu_i)^T \Sigma_i^{-1} (x - \mu_i) + \log \pi_i + C', \qquad (0)
+\end{aligned}$$
+
+where $C$ and $C'$ are constants.
+
+Thus the prediction is done by taking argmax of the above formula.
+
+In training, let $X$, $y$ be the input data, where $X$ is of shape
+$m \times n$, and $y$ of shape $m$. We adopt the convention that each
+row of $X$ is a sample $x^{(i)T}$. So there are $m$ samples and $n$
+features. Denote by $m_i = \#\{j: y_j = i\}$ be the number of samples in
+class $i$. Let $n_c$ be the number of classes.
+
+We estimate $\mu_i$ by the sample means, and $\pi_i$ by the frequencies:
+
+$$\begin{aligned}
+\mu_i &:= {1 \over m_i} \sum_{j: y_j = i} x^{(j)}, \\
+\pi_i &:= \mathbb P(y = i) = {m_i \over m}.
+\end{aligned}$$
+
+Linear discriminant analysis (LDA) is a specialisation of QDA: it
+assumes all classes share the same covariance, i.e. $\Sigma_i = \Sigma$
+for all $i$.
+
+Guassian Naive Bayes is a different specialisation of QDA: it assumes
+that all $\Sigma_i$ are diagonal, since all the features are assumed to
+be independent.
+
+### QDA {#QDA}
+
+We look at QDA.
+
+We estimate $\Sigma_i$ by the variance mean:
+
+$$\begin{aligned}
+\Sigma_i &= {1 \over m_i - 1} \sum_{j: y_j = i} \hat x^{(j)} \hat x^{(j)T}.
+\end{aligned}$$
+
+where $\hat x^{(j)} = x^{(j)} - \mu_{y_j}$ are the centred $x^{(j)}$.
+Plugging this into (0) we are done.
+
+There are two problems that can break the algorithm. First, if one of
+the $m_i$ is $1$, then $\Sigma_i$ is ill-defined. Second, one of
+$\Sigma_i$\'s might be singular.
+
+In either case, there is no way around it, and the implementation should
+throw an exception.
+
+This won\'t be a problem of the LDA, though, unless there is only one
+sample for each class.
+
+### Vanilla LDA {#Vanilla LDA}
+
+Now let us look at LDA.
+
+Since all classes share the same covariance, we estimate $\Sigma$ using
+sample variance
+
+$$\begin{aligned}
+\Sigma &= {1 \over m - n_c} \sum_j \hat x^{(j)} \hat x^{(j)T},
+\end{aligned}$$
+
+where $\hat x^{(j)} = x^{(j)} - \mu_{y_j}$ and ${1 \over m - n_c}$ comes
+from [Bessel\'s
+Correction](https://en.wikipedia.org/wiki/Bessel%27s_correction).
+
+Let us write down the decision function (0). We can remove the first
+term on the right hand side, since all $\Sigma_i$ are the same, and we
+only care about argmax of that equation. Thus it becomes
+
+$$- {1 \over 2} (x - \mu_i)^T \Sigma^{-1} (x - \mu_i) + \log\pi_i. \qquad (1)$$
+
+Notice that we just walked around the problem of figuring out
+$\log \det \Sigma$ when $\Sigma$ is singular.
+
+But how about $\Sigma^{-1}$?
+
+We sidestep this problem by using the pseudoinverse of $\Sigma$ instead.
+This can be seen as applying a linear transformation to $X$ to turn its
+covariance matrix to identity. And thus the model becomes a sort of a
+nearest neighbour classifier.
+
+### Nearest neighbour classifier {#Nearest neighbour classifier}
+
+More specifically, we want to transform the first term of (0) to a norm
+to get a classifier based on nearest neighbour modulo $\log \pi_i$:
+
+$$- {1 \over 2} \|A(x - \mu_i)\|^2 + \log\pi_i$$
+
+To compute $A$, we denote
+
+$$X_c = X - M,$$
+
+where the $i$th row of $M$ is $\mu_{y_i}^T$, the mean of the class $x_i$
+belongs to, so that $\Sigma = {1 \over m - n_c} X_c^T X_c$.
+
+Let
+
+$${1 \over \sqrt{m - n_c}} X_c = U_x \Sigma_x V_x^T$$
+
+be the SVD of ${1 \over \sqrt{m - n_c}}X_c$. Let
+$D_x = \text{diag} (s_1, ..., s_r)$ be the diagonal matrix with all the
+nonzero singular values, and rewrite $V_x$ as an $n \times r$ matrix
+consisting of the first $r$ columns of $V_x$.
+
+Then with an abuse of notation, the pseudoinverse of $\Sigma$ is
+
+$$\Sigma^{-1} = V_x D_x^{-2} V_x^T.$$
+
+So we just need to make $A = D_x^{-1} V_x^T$. When it comes to
+prediction, just transform $x$ with $A$, and find the nearest centroid
+$A \mu_i$ (again, modulo $\log \pi_i$) and label the input with $i$.
+
+### Dimensionality reduction {#Dimensionality reduction}
+
+We can further simplify the prediction by dimensionality reduction.
+Assume $n_c \le n$. Then the centroid spans an affine space of dimension
+$p$ which is at most $n_c - 1$. So what we can do is to project both the
+transformed sample $Ax$ and centroids $A\mu_i$ to the linear subspace
+parallel to the affine space, and do the nearest neighbour
+classification there.
+
+So we can perform SVD on the matrix $(M - \bar x) V_x D_x^{-1}$ where
+$\bar x$, a row vector, is the sample mean of all data i.e. average of
+rows of $X$:
+
+$$(M - \bar x) V_x D_x^{-1} = U_m \Sigma_m V_m^T.$$
+
+Again, we let $V_m$ be the $r \times p$ matrix by keeping the first $p$
+columns of $V_m$.
+
+The projection operator is thus $V_m$. And so the final transformation
+is $V_m^T D_x^{-1} V_x^T$.
+
+There is no reason to stop here, and we can set $p$ even smaller, which
+will result in a lossy compression / regularisation equivalent to doing
+[principle component
+analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) on
+$(M - \bar x) V_x D_x^{-1}$.
+
+### Fisher discriminant analysis {#Fisher discriminant analysis}
+
+The Fisher discriminant analysis involves finding an $n$-dimensional
+vector $a$ that maximises between-class covariance with respect to
+within-class covariance:
+
+$${a^T M_c^T M_c a \over a^T X_c^T X_c a},$$
+
+where $M_c = M - \bar x$ is the centred sample mean matrix.
+
+As it turns out, this is (almost) equivalent to the derivation above,
+modulo a constant. In particular, $a = c V_m^T D_x^{-1} V_x^T$ where
+$p = 1$ for arbitrary constant $c$.
+
+To see this, we can first multiply the denominator with a constant
+${1 \over m - n_c}$ so that the matrix in the denominator becomes the
+covariance estimate $\Sigma$.
+
+We decompose $a$: $a = V_x D_x^{-1} b + \tilde V_x \tilde b$, where
+$\tilde V_x$ consists of column vectors orthogonal to the column space
+of $V_x$.
+
+We ignore the second term in the decomposition. In other words, we only
+consider $a$ in the column space of $V_x$.
+
+Then the problem is to find an $r$-dimensional vector $b$ to maximise
+
+$${b^T (M_c V_x D_x^{-1})^T (M_c V_x D_x^{-1}) b \over b^T b}.$$
+
+This is the problem of principle component analysis, and so $b$ is first
+column of $V_m$.
+
+Therefore, the solution to Fisher discriminant analysis is
+$a = c V_x D_x^{-1} V_m$ with $p = 1$.
+
+### Linear model {#Linear model}
+
+The model is called linear discriminant analysis because it is a linear
+model. To see this, let $B = V_m^T D_x^{-1} V_x^T$ be the matrix of
+transformation. Now we are comparing
+
+$$- {1 \over 2} \| B x - B \mu_k\|^2 + \log \pi_k$$
+
+across all $k$s. Expanding the norm and removing the common term
+$\|B x\|^2$, we see a linear form:
+
+$$\mu_k^T B^T B x - {1 \over 2} \|B \mu_k\|^2 + \log\pi_k$$
+
+So the prediction for $X_{\text{new}}$ is
+
+$$\text{argmax}_{\text{axis}=0} \left(K B^T B X_{\text{new}}^T - {1 \over 2} \|K B^T\|_{\text{axis}=1}^2 + \log \pi\right)$$
+
+thus the decision boundaries are linear.
+
+This is how scikit-learn implements LDA, by inheriting from
+`LinearClassifierMixin` and redirecting the classification there.
+
+Implementation {#Implementation}
+--------------
+
+This is where things get interesting. How do I validate my understanding
+of the theory? By implementing and testing the algorithm.
+
+I try to implement it as close as possible to the natural language /
+mathematical descriptions of the model, which means clarity over
+performance.
+
+How about testing? Numerical experiments are harder to test than
+combinatorial / discrete algorithms in general because the output is
+less verifiable by hand. My shortcut solution to this problem is to test
+against output from the scikit-learn package.
+
+It turned out to be harder than expected, as I had to dig into the code
+of scikit-learn when the outputs mismatch. Their code is quite
+well-written though.
+
+The result is
+[here](https://github.com/ycpei/machine-learning/tree/master/discriminant-analysis).
+
+### Fun facts about LDA {#Fun facts about LDA}
+
+One property that can be used to test the LDA implementation is the fact
+that the scatter matrix $B(X - \bar x)^T (X - \bar X) B^T$ of the
+transformed centred sample is diagonal.
+
+This can be derived by using another fun fact that the sum of the
+in-class scatter matrix and the between-class scatter matrix is the
+sample scatter matrix:
+
+$$X_c^T X_c + M_c^T M_c = (X - \bar x)^T (X - \bar x) = (X_c + M_c)^T (X_c + M_c).$$
+
+The verification is not very hard and left as an exercise.