2020/06/14
Embedding Many Time Series via Recurrence Plots
We demonstrate how recurrence plots can be used to embed a large set of time series via UMAP and HDBSCAN to quickly identify groups of series with unique characteristics such as seasonality or outliers. The approach supports exploratory analysis of time series via visualization that scales poorly when combined with large sets of related time series. We show how it works using a Walmart dataset of sales and a Citi Bike dataset of bike rides.
2020/04/26
Are You Sure This Embedding Is Good Enough?
Suppose you are given a data set of five images to train on, and then have to classify new images with your trained model. Five training samples are in general not sufficient to train a state-of-the-art image classification model, thus this problem is hard and earned it’s own name: few-shot image classification. A lot has been written on few-shot image classification and complex approaches have been suggested.1 Tian et al.
2018/07/25
SVD for a Low-Dimensional Embedding of Instacart Products
Building on the Instacart product recommendations based on Pointwise Mutual Information (PMI) in the previous article, we use Singular Value Decomposition to factorize the PMI matrix into a matrix of lower dimension (“embedding”). This allows us to identify groups of related products easily. We finished the previous article with a long table where every row measured how surprisingly often two products were bought together according to the Instacart Online Grocery Shopping dataset.