Generative Classification¶

• [1] You have a machine that measures property $x$, the "orangeness" of liquids. You wish to discriminate between $C_1 = \text{Fanta'}$ and $C_2 = \text{Orangina'}$. It is known that
\begin{align*} p(x|C_1) &= \begin{cases} 10 & 1.0 \leq x \leq 1.1\\ 0 & \text{otherwise} \end{cases}\\ p(x|C_2) &= \begin{cases} 200(x - 1) & 1.0 \leq x \leq 1.1\\ 0 & \text{otherwise} \end{cases} \end{align*}

The prior probabilities $p(C_1) = 0.6$ and $p(C_2) = 0.4$ are also known from experience.
(a) (##) A "Bayes Classifier" is given by

$$\text{Decision} = \begin{cases} C_1 & \text{if } p(C_1|x)>p(C_2|x) \\ C_2 & \text{otherwise} \end{cases}$$

Derive the optimal Bayes classifier.
(b) (###) The probability of making the wrong decision, given $x$, is

$$p(\text{error}|x)= \begin{cases} p(C_1|x) & \text{if we decide C_2}\\ p(C_2|x) & \text{if we decide C_1} \end{cases}$$

Compute the total error probability $p(\text{error})$ for the Bayes classifier in this example.

(a) We choose $C_1$ if $p(C_1|x)/p(C_2|x) > 1$. This condition can be worked out as $$\frac{p(C_1|x)}{p(C_2|x)} = \frac{p(x|C_1)p(C_1)}{p(x|C_2)p(C_2)} = \frac{10 \times 0.6}{200(x-1)\times 0.4}>1$$ which evaluates to choosing \begin{align*} C_1 &\quad \text{ if 1.0\leq x < 1.075}\\ C_2 &\quad \text{ if 1.075 \leq x \leq 1.1 } \end{align*} The probability that $x$ falls outside the interval $[1.0,1.1]$ is zero.
(b) The total probability of error $p(\text{error})=\int_x p(\text{error}|x)p(x) \mathrm{d}{x}$. We can work this out as

\begin{align*} p(\text{error}) &= \int_x p(\text{error}|x)p(x)\mathrm{d}{x}\\ &= \int_{1.0}^{1.075} p(C_2|x)p(x) \mathrm{d}{x} + \int_{1.075}^{1.1} p(C_1|x)p(x) \mathrm{d}{x}\\ &= \int_{1.0}^{1.075} p(x|C_2)p(C_2) \mathrm{d}{x} + \int_{1.075}^{1.1} p(x|C_1)p(C_1) \mathrm{d}{x}\\ &= \int_{1.0}^{1.075}0.4\cdot 200(x-1) \mathrm{d}{x} + \int_{1.075}^{1.1} 0.6\cdot 10 \mathrm{d}{x}\\ &=80\cdot[x^2/2-x]_{1.0}^{1.075} + 6\cdot[x]_{1.075}^{1.1}\\ &=0.225 + 0.15\\ &=0.375 \end{align*}
• [2] (#) (see Bishop exercise 4.8): Using (4.57) and (4.58) (from Bishop's book), derive the result (4.65) for the posterior class probability in the two-class generative model with Gaussian densities, and verify the results (4.66) and (4.67) for the parameters $w$ and $w0$.

Substitute 4.64 into 4.58 to get
\begin{align*} a &= \log \left( \frac{ \frac{1}{(2\pi)^{D/2}} \cdot \frac{1}{|\Sigma|^{1/2}} \cdot \exp\left( -\frac{1}{2}(x-\mu_1)^T \Sigma^{-1} (x-\mu_1)\right) \cdot p(C_1)}{\frac{1}{(2\pi)^{D/2}} \cdot \frac{1}{|\Sigma|^{1/2}}\cdot \exp\left( -\frac{1}{2}(x-\mu_2)^T \Sigma^{-1} (x-\mu_2)\right) \cdot p(C_2)}\right) \\ &= \log \left( \exp\left(-\frac{1}{2}(x-\mu_1)^T \Sigma^{-1} (x-\mu_1) + \frac{1}{2}(x-\mu_2)^T \Sigma^{-1} (x-\mu_2) \right) \right) + \log \frac{p(C_1)}{p(C_2)} \\ &= ... \\ &=( \mu_1-\mu_2)^T\Sigma^{-1}x - 0.5\left(\mu_1^T\Sigma^{-1}\mu_1 - \mu_2^T\Sigma^{-1} \mu_2\right)+ \log \frac{p(C_1)}{p(C_2)} \end{align*} Substituting this into the right-most form of (4.57) we obtain (4.65), with $w$ and $w0$ given by (4.66) and (4.67), respectively.

• [3] (###) (see Bishop exercise 4.9).

The Log-likelihood is given by $$\log p(\{\phi_n,t,n\} | \{\pi_k\}) = \sum_n \sum_k t_{nk}\left(\log p(\phi_n|C_k) + \log \pi_k\right)\,.$$ Using the method of Lagrange multipliers (Bishop app.E), we augment the log-likelihood with the constraint and obtain the Lagrangian $$\log p(\{\phi_n,t_{nk}\} | \{\pi_k\})+\lambda \left(\sum_k \pi_k -1 \right)\,.$$ In order to maximize, we set the derivative with respect to $\pi_k$ equal to zero and obtain \begin{align*} \sum_n \frac{t_{nk}}{\pi_k}+\lambda &=0 \\ -\pi_k\lambda &=\sum_n t_{nk} = N_k \\ -\lambda \sum_k \pi_k &= \sum_k \sum_n t_{nk} \\ \lambda &= -N \end{align*}

• [4] (##) (see Bishop exercise 4.10).

We can write the log-likelihood as \begin{align*} \log p(\{\phi_n,t_n\}|\{\pi_k\}) \propto -0.5\sum_n\sum_kt_{nk}\left(\log |\Sigma|+(\phi_n-\mu_k)^T\Sigma^{-1}(\phi-\mu)\right) \end{align*} The derivatives of the likelihood with respect to mean and shared covariance are respectively \begin{align*} \nabla_{\mu_k}\log p(\{\phi_n,t_n\}|\{\pi_k\}) &= \sum_n\sum_k t_{nk}\Sigma^{-1}\left(\phi_n-\mu_k\right) = 0 \\ \sum_n t_{nk}\left(\phi_n-\mu_k\right))&=0 \\ \mu_k &= \frac{1}{N_k}\sum_n t_{nk}\phi_n \\ \nabla_{\Sigma}\log p(\{\phi_n,t_n\}|\{\pi_k\})&=\sum_n\sum_k t_{nk}\left(\Sigma - (\phi_n-\mu_k)(\phi_n-\mu_k)^T\right) = 0 \\ \sum_n\sum_k t_{nk}\Sigma &= \sum_n\sum_k t_{nk}(\phi_n-\mu_k)(\phi_n-\mu_k)^T \\ \Sigma &= \frac{1}{N}\sum_k\sum_n t_{nk}(\phi_n-\mu_k)(\phi_n-\mu_k)^T \end{align*}

Discriminative Classification¶

• [1] Given a data set $D=\{(x_1,y_1),\ldots,(x_N,y_N)\}$, where $x_n \in \mathbb{R}^M$ and $y_n \in \{0,1\}$. The probabilistic classification method known as logistic regression attempts to model these data as $$p(y_n=1|x_n) = \sigma(\theta^T x_n + b)$$ where $\sigma(x) = 1/(1+e^{-x})$ is the logistic function. Let's introduce shorthand notation $\mu_n=\sigma(\theta^T x_n + b)$. So, for every input $x_n$, we have a model output $\mu_n$ and an actual data output $y_n$.
(a) Express $p(y_n|x_n)$ as a Bernoulli distribution in terms of $\mu_n$ and $y_n$.
(b) If furthermore is given that the data set is IID, show that the log-likelihood is given by $$L(\theta) \triangleq \log p(D|\theta) = \sum_n \left\{y_n \log \mu_n + (1-y_n)\log(1-\mu_n)\right\}$$
(c) Prove that the derivative of the logistic function is given by $$\sigma^\prime(\xi) = \sigma(\xi)\cdot\left(1-\sigma(\xi)\right)$$
(d) Show that the derivative of the log-likelihood is $$\nabla_\theta L(\theta) = \sum_{n=1}^N \left( y_n - \sigma(\theta^T x_n +b)\right)x_n$$
(e) Design a gradient-ascent algorithm for maximizing $L(\theta)$ with respect to $\theta$.

(a) $p(y_n|x_n) = p(y_n=1|x_n)^{y_n} p(y_n=0|x_n)^{1-y_n} = \mu_n^{y_n}(1-\mu_n)^{1-y_n}$
(b) The log-likelihood is given by \begin{align*} L(\theta) &= \log p(D|\theta) = \sum_n \log p(y_n|x_n,\theta)\\ &= \sum_n \left\{y_n \log \mu_n + (1-y_n)\log(1-\mu_n)\right\} \end{align*}
(c) \begin{align*} \frac{d{}}{d{x}}\left( \frac{1}{1+e^{-x}}\right) &= \frac{(1+e^{-x})\cdot 0 - (-e^{-x}\cdot 1)}{(1+e^{-x})^2}\\ &= \frac{e^{-x}}{(1+e^{-x})^2} = \frac{1}{1+e^{-x}}\cdot \frac{e^{-x}}{1+e^{-x}}\\ &=\sigma(x)\left( 1-\sigma(x)\right) \end{align*}
(d) \begin{align*} \nabla_\theta L(\theta) &= \sum_n \frac{\partial{L}}{\partial{\mu_n}}\cdot \frac{\partial{\mu_n}}{\partial{(\theta^T x_n +b)}} \cdot \frac{\partial{(\theta^T x_n +b)}}{\partial{\theta}}\\ &= \sum_n \left(\frac{y_n}{\mu_n} - \frac{1-y_n}{1-\mu_n} \right) \cdot \mu_n(1-\mu_n) \cdot x_n\\ &= \sum_n \frac{y_n - \mu_n}{\mu_n(1-\mu_n)} \cdot \mu_n(1-\mu_n) \cdot x_n\\ &= \sum_n (y_n - \mu_n) \cdot x_n \end{align*}
(e) $$\theta^{(t+1)} = \theta^{(t)} + \rho \sum_n (y_n - \mu_n^{(t)})x_n$$

• [2] Describe shortly the similarities and differences between the discriminative and generative approach to classification.

Both aim to build an algorithm for $p(y|x)$ where $y$ is a discrete class label and $x$ is a vector of real (or possibly discretely valued) variables. In the discriminative approach, we propose a model $p(y|x,\theta)$ and use a training data set $D=\{(x_1,y_1),(x_2,y_2),\ldots,(x_N,y_N)\}$ to infer good values for the parameters. For instance, in a maximum likelihood setting, we choose the parameters $\hat{\theta}$ that maximize $p(D|\theta)$. The classification algorithm is now given by $$p(y|x) = p(y|x,\hat{\theta})\,.$$ In the generative approach, we also aim to design an algorithm $p(y|x)$ through a parametric model that is now given by $p(y,x|\theta) = p(x|y,\theta)p(y|\theta)$. Again, we use the data set to train the parameters, eg, $\hat{\theta} = \arg\max_\theta p(D|\theta)$, and the classification algorithm is now given by Bayes rule: $$p(y|x) \propto p(x|y,\hat{\theta})\cdot p(y|\hat{\theta})$$

• [3] (Bishop ex.4.7) (#) Show that the logistic sigmoid function $\sigma(a) = \frac{1}{1+\exp(-a)}$ satisfies the property $\sigma(-a) = 1-\sigma(a)$ and that its inverse is given by $\sigma^{-1}(y) = \log\{y/(1-y)\}$.

\begin{align*} 1- \sigma(a) &= 1 - \frac{1}{1 + \exp(-a)} = \frac{1+\exp(-a) - 1}{1+\exp(-a)} \\ &= \frac{\exp(-a)}{1+\exp(-a)} = \frac{1}{\exp(a)+1} = \sigma(-a)\end{align*}

Regarding the inverse, \begin{align*} y = \sigma(a) &= \frac{1}{1+\exp(-a)} \\ \Rightarrow \frac1y - 1 &= \exp(-a) \\ \Rightarrow \log\left( \frac{1-y}{y}\right) &= -a \\ \Rightarrow \log\left( \frac{y}{1-y}\right) &= a = \sigma^{-1}(y) \end{align*}

• [4] (Bishop ex.4.16) (###) Consider a binary classification problem in which each observation $x_n$ is known to belong to one of two classes, corresponding to $y_n = 0$ and $y_n = 1$. Suppose that the procedure for collecting training data is imperfect, so that training points are sometimes mislabelled. For every data point $x_n$, instead of having a value $y_n$ for the class label, we have instead a value $\pi_n$ representing the probability that $y_n = 1$. Given a probabilistic model $p(y_n = 1|x_n,\theta)$, write down the log-likelihood function appropriate to such a data set.

If the values of the $\{y_n\}$ were known then each data point for which $y_n = 1$ would contribute $\log p(y_n = 1|x_n,\theta)$ to the log likelihood, and each point for which $y_n = 0$ would contribute $\log p(y_n=0|x_n,\theta) = \log(1 − p(y_n = 1|x_n,\theta))$ to the log likelihood. A data point whose probability of having $y_n = 1$ is given by $\pi_n$ will therefore contribute $$\pi_n \log p(y_n=1|x_n,\theta) + (1 - \pi_n) \log p(y_n=0|x_n,\theta)$$ and so the overall log-likelihood given the data set is $$\sum_{n=1}^N \pi_n \log p(y_n=1|x_n,\theta) + (1 - \pi_n) \log p(y_n=0|x_n,\theta)$$

• [5] (###) Let $X$ be a real valued random variable with probability density $$p_X(x) = \frac{e^{-x^2/2}}{\sqrt{2\pi}},\quad\text{for all x}.$$ Also $Y$ is a real valued random variable with conditional density $$p_{Y|X}(y|x) = \frac{e^{-(y-x)^2/2}}{\sqrt{2\pi}},\quad\text{for all x and y}.$$ (a) Give an (integral) expression for $p_Y(y)$. Do not try to evaluate the integral.
(b) Approximate $p_Y(y)$ using the Laplace approximation. Give the detailed derivation, not just the answer. Hint: You may use the following results. Let $$g(x) = \frac{e^{-x^2/2}}{\sqrt{2\pi}}$$ and $$h(x) = \frac{e^{-(y-x)^2/2}}{\sqrt{2\pi}}$$ for some real value $y$. Then: \begin{align*} \frac{\partial}{\partial x} g(x) &= -xg(x) \\ \frac{\partial^2}{\partial x^2} g(x) &= (x^2-1)g(x) \\ \frac{\partial}{\partial x} h(x) &= (y-x)h(x) \\ \frac{\partial^2}{\partial x^2} h(x) &= ((y-x)^2-1)h(x) \end{align*}

(a) $$p_Y(y) = \int_{-\infty}^{\infty} p_X(x)p_{Y|X}(y|x)\,dx = \int_{-\infty}^{\infty} \frac{e^{-\frac12(x^2+(y-x)^2)}}{2\pi}\,dx$$ (b) Using the hint we determine the first derivative of \begin{align*} f(x) &= g(x)h(x), \\ \frac{\partial}{\partial x} f(x) &= \frac{\partial}{\partial x} g(x)\cdot h(x) = -xg(x)h(x)+g(x)(y-x)h(x) = (y-2x)f(x) \end{align*}
Setting this to zero gives \begin{align*} y-2x&= 0; \quad \text{so}\quad x=\frac12y. \\ \frac{\partial}{\partial x} \ln f(x) &= \frac{\frac{\partial}{\partial x} f(x)}{f(x)} = (y-2x) \\ \frac{\partial^2}{\partial x^2} \ln f(x) &= \frac{\partial}{\partial x} (y-2x) = -2. \end{align*} So, we find $A=2$, see lecture notes, and thus \begin{align*} p_Y(y) &= \int_{-\infty}^{\infty}f(x)\,dx\approx f(\frac{y}{2})\sqrt{\frac{2\pi}{A}} \\ &= g(\frac{y}{2})h(\frac{y}{2})\sqrt{\frac{2\pi}{A}} \\ &= \frac{1}{\sqrt{2\pi\cdot2}}e^{-y^2/4}. \end{align*} So $Y$ is a Gaussian with mean $m=0$ and variance $\sigma^2=2$.

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