# Week 2: d3.js and line detection

This week’s meeting focused on talks given by Lisa and Jeeyoung.

Lisa talked first, discussing her latest project using Data-Driven Documents (d3).  The dataset of choice was the Iris dataset, that which any R enthusiast will be familiar with.  It contains five variables: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.

For those unfamiliar with d3.js, it is a visualization library that provides functionality to bind data to objects.  The advantages of using d3.js is that it is extremely fast and flexible.

Here is a simple example of selecting a circle and updating it with new coordinates and size :

var circle = svg.selectAll(“circle”)
.attr(“cy”, 90)
.attr(“cx”, 30)
.attr(“r”, 40);

But what if we want to make it more interesting.  Consider a data set of size two (i.e [20, 35]).  D3.js makes it easy to add a new circle and visualize the data in such a way that the size and x-coordinate of each circle reflects each data point.

var circle = svg.selectAll(“circle”)
.data([20, 35])
.enter().append(“circle”)
.attr(“cy”, 90)
.attr(“cx”, function(d) {return d;})
.attr(“r”, function(d) {return d;});

Lisa’s project explored multiple visualization strategies including an easy-to-use interface to change the axis of a scatter plot.

Jeeyoung discussed edge detection and line detection.  In edge detection, a user applies an algorithm (that uses a threshold on the directional derivatives) to produce an image with the original’s edges.  One of the more common edge detection algorithms is Canny edge detection (see below: source Wikipedia).

From there, you can apply line detection.  It is easier to do this by mapping each point along the edges to a line in the parameter space (slope-intercept coordinate plane) and looking for intersections.  While the exact details will not be mentioned, the transformation to be used is the Hough Transformation.

Note you can implement both using Matlab commands : edge(.) and hough(.)

As usual come join us in the Stats Club Office every Monday at 4:30pm!

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# Advertisement Bidding, Kaggle Contest, New Term & New Faces

Today, the Data Science team kicked off its first meeting of 2012! Welcome to Arthur, David, Paul, and Will for joining the cause! The meeting covered exciting new topics ranging from internet ad bidding to Kaggle competition.

Advertisement Bidding: Paul talked about internet ad bidding, i.e. bidding on advertisement slots on platforms like Google AdWords. He introduced the optimization problem inherent to ad bidding, the difference in pricing between advertisement slots on Google, and explained various bidding methods.

Stay Alert! Kaggle Competition: David gave a walk-through of the Stay Alert! Ford challenge on Kaggle. He described two main findings which propelled his model to finish 6th in the overall rankings. The first was a data visualization method which, in his case, zeroed out the randomly added datapoints. The second was the discovery of interaction effects between variables.

The next meeting will be held next Monday at 4:30pm in the Stats Club Office M3 3109.

# Gaussian Mixture Model, K-means algorithm, and how to solve them.

Introduction

Mixture model is a weighted combinations of probability distributions. Mixture model is a powerful and well-understood tool for various problems in artificial intelligence, computer vision, and statistics. In this post, we will examine Gaussian mixture models, and algorithms to solve them.

Let’s first introduce Gaussian Mixture Model. Let $(\mu_j, \sigma_j)$ be a collection of Gaussian distributions, with some associated weights $\pi_j \in [0,1]$. Let $(x_i)_{i=1}^n$ be set of observations that are generated by the above distributions.

$P(x_i|\sigma_j) \sim \pi_i G(\mu_j, \sigma_j)$

Figure 1. Mixture of two Gaussian distributions, with $\pi = (0.25, 0.75)$, $\mu = (0, 5)$, $\sigma=(1,3)$.

We call this model Mixture of Gaussians. The parameter $\pi$ is called the mixing portion of the Gaussians.

By solving Gaussian Mixture Model, we are given a set of observed points $X=(x_i)_{i=1}^n$ and we want to find the model parameters $(\pi, \mu, \sigma)$ that maximizes the posterior probability $P(X | \pi,\mu,\sigma)$.

Figure 2. Histogram of 10,000 points generated by the above Gaussian mixture model. We want to find the model parameters which maximizes the posterior probability of generating those points.

K-means clustering

K-means clustering solves a different problem, but K-means problem gives an insight over how we can solve the mixture of Gaussian problem. In K-means clustering, a set of observed points $(x_i)_{i=1}^{n}$, a positive integer $k$ are given. We want to cluster the points into $k$ clusters $(S_j)_{j=1}^{k}$ so that it minimizes the within cluster sum of squares (WCSS).

$\underset{S_x}{argmin}\sum_{j=1}^{k} \sum_{x \in S_j} || x - \mu_j ||^2$

$\mu_j = \frac{\sum_{x \in S_j} x}{\#S_j}$ is the mean of the cluster $S_j$.

Figure 3. Example of a K-means clustering in 2 dimensions, K = 2. The observed points are clustered into the two clusters. The X mark denotes the centre of the cluster.

Figure 4. Another example of a K-means clustering in 2 dimensions, K=4.

Expectation Maximization (EM)

Expectation Maximization is an iterative algorithm for solving maximum likelihood problems. Given the set of observed data X, generated by the probability model with parameter 𝜃, we want to find the parameter 𝜃 that maximizes the posterior probability $P(X|\theta)$. In K-means clustering,

• $X$ – observed data $(x_i)_{i=1}^{n}$
• $\theta$ – the clustering of the observed data points $(S_j)_{j=1}^{k}$, and their associated centres $\mu_j$.

EM Algorithm starts with an initial hypothesis for the parameter 𝜃 and iteratively calculate the posterior probability $P(X|\theta)$, and re-calculates the hypothesis 𝜃 each step. The details are going to be different between each EM algorithms, but the following are the approximate steps.

1. Start with an initial hypothesis 𝜃.
2. (Expectation step) Calculate the posterior probability $P(X | \theta)$.
3. (Maximization step) Categorize the observed data according to the probability, and update the hypothesis accordingly.
4. Repeat this process until the hypothesis converges.

Algorithm – Hard K-means

We assume that the $P(x_i|S_j)$, the likelihood of point $x_i$ belonging to the cluster centered at the point $\mu_j$ is 1 iff $\mu_j$ is the closest cluster.

$P(x_i|S_j) = 1\ if\ j = \underset{k}{argmin} || x_i - \mu_k ||, 0\ otherwise$

During the E-step, the distances $|| x_i - \mu_j ||$ are calculated to determine $P(x_i|S_j)$. During the M-step$S_j$ are determined by clustering each $x_i$ to the cluster closest to it. Finally, each $\mu_j$ are re-calculated by taking the mean of the points in $S_j$.

$\mu_j = \displaystyle\sum\limits_{x \in S_j}\frac{x}{\#S_j}$

Figure 5. Example of a K-means algorithm, for K=2. Initial cluster centres are randomly chosen from the observed points. Each iteration, the observed points are categorized to the nearest cluster centres, and the cluster centres are re-calculated. This process is repeated until the cluster centres converge.

Algorithm – Soft K-means

In Hard-K means. the observed points can only belong in a single cluster. However, it may be useful to consider the probability $P(x_i|S_j)$ during the computation of $\mu$. This is especially true for the points near a boundary between two clusters.

We want to calculate $\mu_j$ via the weighted average of $x_i$ with $P(x_i|S_j)$.

We assume that the likelihood $P(x_i|S_j)$ has exponential distribution with the stiffness factor $\beta > 0$.

$P(x_i|S_j) \sim e^{-\beta || x_i - \mu_j ||}$

This way, $P(x_i|S_j)$ is still monotonically decreasing with respect to the distance away from the centres, but the probability is panellized for the higher distance.

Similar to the Hard K-means algorithm, $P(x_i|S_j)$ is obtained by normalizing this value.

$P(x_i|S_j) = \frac{e^{-\beta||x_i-\mu_j||}}{\sum_l e^{-\beta || x_l - \mu_j ||}}$

$\mu_j$ is obtained by the weighted averages of all $x_i$.

$\mu_j = \frac{\sum_i P(x_i|S_j) x_i}{\sum_i P(x_i|S_j)}$

One thing to note is that Hard K-mean algorithm is equivalent to a Soft K-mean algorithm as $\beta \rightarrow \infty$.

Algorithm – Gaussian K-means

K-means algorithms are great, but the algorithm only reveals information about the cluster membership. However, we can modify the EM algorithm to calculate

We come back to the original assumption, that the likelihood $P(x_i|S_j)$ follows a Gaussian distribution.

$P(x_i|S_j) \sim \pi_j G(||x_i - \mu_j||, \sigma_j)$

Where $G(\bullet, \bullet)$ is the Gaussian probability density distribution. The actual probably is calculated as following.

$P(x_i|S_j) = \frac{\pi_j G(||x_i-\mu_j||, \sigma_j)}{\sum_k \pi_k G(||x_i-\mu_k||, \sigma_k)}$

We have to re-calculate three parameters, $\mu, \sigma, \pi$. Similar to the Soft K-mean algorithm, μ is the weighted averages of all $x_i$. $\pi$ is the normalized ratio fo the sums $\sum_i P(x_i|S_j)$. 𝜎 is the weighted standard deviation.

$\mu_j = \frac{\sum_i P(x_i|S_j) x_i}{R_k}$.

$\sigma_j^2 = \frac{\sum_i P(x_i|S_j)|| x_i - \mu_j || }{I R_k}$

$\pi_j = \frac{R_j}{\sum_k R_k}$

$R_j = \sum_i P(x_i|S_j)$

Where I is the dimensionality of x.

Figure 6. Points sampled from a mixture of two Gaussians. Top left plot is the data points generated by the model parameters $\pi=(\frac{1}{2},\frac{1}{2})$, $\sigma_x = \sigma_y = (0.3, 1)$ $\mu_x = (-2, 0), \mu_y = (0, 0)$. The other plots show the results of Hard K-means, Soft K-means, and Gaussian K-means.

Figure 7.  Another example of Gaussian K-means algorithm, with 4 clusters.

References

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