Blog Archives

Clusters Powerful Enough to Generate Their Own Subspaces

May 20, 2015
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Clusters Powerful Enough to Generate Their Own Subspaces

Cluster are groupings that have no external label. We start with entities described by a set of measurements but no rule for sorting them by type. Mixture modeling makes this point explicit with its equation showing how each measurement is an independe...

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What is Data Science? Can Topic Modeling Help?

May 13, 2015
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What is Data Science? Can Topic Modeling Help?

Predictive analytics often serves as an introduction to data science, but it may not be the best exemplar given its long history and origins in statistics. David Blei, on the other hand, struggles to define data science through his work on topic modeli...

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Centering and Standardizing: Don’t Confuse Your Rows with Your Columns

May 11, 2015
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Centering and Standardizing: Don’t Confuse Your Rows with Your Columns

R uses the generic scale( ) function to center and standardize variables in the columns of data matrices. The argument center=TRUE subtracts the column mean from each score in that column, and the argument scale=TRUE divides by the column standard devi...

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What Can We Learn from the Apps on Your Smartphone? Topic Modeling and Matrix Factorization

May 8, 2015
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What Can We Learn from the Apps on Your Smartphone? Topic Modeling and Matrix Factorization

The website for The Burning House begins with a simple question:If your house was burning, what would you take with you? It's a conflict between what's practical, valuable and sentimental. What you would take reflects your interests, background and pri...

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Clusters May Be Categorical but Cluster Membership Is Not All-or-None

May 4, 2015
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Clusters May Be Categorical but Cluster Membership Is Not All-or-None

Very early in the study of statistics and R, we learn that random variables can be either categorical or continuous. Regrettably, we are forced to relearn this distinction over and over again as we debug error messages produced by our code (e.g., ...

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Modeling the Latent Structure That Shapes Brand Learning

April 29, 2015
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Modeling the Latent Structure That Shapes Brand Learning

What is a brand? Metaphorically, the brand is the white sphere in the middle of this figure, that is, the ball surrounded by the radiating black cones. Of course, no ball has been drawn, just the conic thorns positioned so that we construct the sphere ...

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Conjoint Analysis and the Strange World of All Possible Feature Combinations

April 22, 2015
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Conjoint Analysis and the Strange World of All Possible Feature Combinations

The choice modeler looks over the adjacent display of cheeses and sees the joint marginal effects of the dimensions spanning the feature space: milk source, type, origin, moisture content, added mold or bacteria, aging, salting, packaging, price, and m...

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Recommending Recommender Systems When Preferences Are Not Driven By Simple Features

April 15, 2015
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Recommending Recommender Systems When Preferences Are Not Driven By Simple Features

Why does lifting out a slice make the pizza appear more appealing?We can begin our discussion with the ultimate feature bundle - pizza toppings. Technically, a menu would only need to list all the toppings and allow the customers to build their own piz...

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Modeling Categories with Breadth and Depth

April 10, 2015
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Modeling Categories with Breadth and Depth

Religion is a categorical variable with followers differentiated by their degree of devotion. Liberals and conservatives check their respective boxes when surveyed, although moderates from each group sometimes seem more alike than their more extreme co...

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What Consumers Learn Before Deciding to Buy: Representation Learning

March 20, 2015
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What Consumers Learn Before Deciding to Buy: Representation Learning

Features form the basis for much of our preference modeling. When asked to explain one's preferences, features are typically accepted as appropriate reasons: this job paid more, that candidate supports tax reform, or it was closer to home. We believe t...

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