This post is authored by Nile Wilson, Software Engineer Intern at Microsoft.

In Part 1 of this blog post series, we examined use cases for visual search and how visual search works. Now we’ll extend the results of Part 1 from the digital world to the physical world using AWS DeepLens, a deep-learning-enabled video camera. Most current applications of visual search don’t involve direct interaction with the physical world in real time. Instead, visual search typically happens within the digital world of websites and apps that submit a static image for a visual search query.

This is the anti-Wolfram.

This is a guest blog post by Nathan Sutton, Chief Architect at TIBCO. In their own words, “TIBCO fuels digital business by enabling better decisions and faster, smarter actions through the TIBCO Connected Intelligence Cloud. From APIs and systems to devices and people, we interconnect everything, capture data in real time wherever it is, and augment the intelligence of your business through analytical insights.“

Robust Matrix Elastic net Based Canonical Correlation Analysis (RMEN-CCA) This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different ‘views’. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches. …

Extra String Manipulation Functions (strex)There are some things that I wish were easier with the ‘stringr’ or ‘stringi’ packages. The foremost of these is the extraction of numbers from strings …

I happened to be reading an old issue of Private Eye (a friend subscribes and occasionally gives me some old copies) and came across this, discussing various misinformation regarding a recent crime that had been reported by a London tabloid columnist named Rod Liddle (no relation to the famous statistician, I assume):

I just got back from Fluidride’s 2018 Alps trip where we rode the Swiss Italian, and French alps. Coming from the PNW I always find myself comparing destination trails to what I have in my backyard back home. You want awkward and rowdy: North Shore, Long shuttle-able diverse downhill: Tres Hombres, Legit freeride and fortitude challenging rock rolls: Squamish, skill park progression for all levels 30min from Seattle: Duthie Hill. The list goes on and on, the PNW has it all! I didn’t expect in the alps to offer the same thing but all crammed in one area at incredible scale and grandeur.

Over a year ago, my fellow data infrastructure engineers and I broke ground on a total rewrite of our event delivery infrastructure. Our mission was to build a robust, centralized data integration platform tailored to the needs of our Data Scientists. The platform would be fully self-service, so as to maximize the Data Scientists’ autonomy and give them complete control over their event data. Ultimately, we delivered a platform that is revolutionizing the way Data Scientists interact with Stitch Fix’s data.

Denoising Random Forest This paper proposes a novel type of random forests called a denoising random forests that are robust against noises contained in test samples. Such noise-corrupted samples cause serious damage to the estimation performances of random forests, since unexpected child nodes are often selected and the leaf nodes that the input sample reaches are sometimes far from those for a clean sample. Our main idea for tackling this problem originates from a binary indicator vector that encodes a traversal path of a sample in the forest. Our proposed method effectively employs this vector by introducing denoising autoencoders into random forests. A denoising autoencoder can be trained with indicator vectors produced from clean and noisy input samples, and non-leaf nodes where incorrect decisions are made can be identified by comparing the input and output of the trained denoising autoencoder. Multiple traversal paths with respect to the nodes with incorrect decisions caused by the noises can then be considered for the estimation. …