# David Torpey

Machine Learning Researcher, Software Developer and Computer Vision Enthusiast

## Reducing the dimensionality of data with neural networks

Reducing the dimensionality of data has many valuable potential uses. The low-dimensional version of the data can be used for visualisation, or for further processing in a modelling pipeline. The low-dimensional version should capture only the salient features of the data, and can indeed be seen as a form of compression. Many techniques for dimensionality reduction exists, including PCA (and its kernelized variant Kernel PCA), Locally Linear Embedding, ISOMAP, UMAP, Linear Discriminant Analysis, and t-SNE. Some of these are linear methods, while others are non-linear methods. Many of the non-linear methods falls into a class of algorithms known as manifold learning algorithms.

deep learning   autoencoder   dimensionality reduction

## FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Labelled data is often either expensive or hard to obtain. As such, there has been a plethora of work to make better use of unlabelled data in machine learning, with paradigms such as unsupervised learning, semi-supervised learning, and more recently, self-supervised learning. FixMatch is an approach to semi-supervised learning (SSL) that combines two common approaches of SSL: 1. consistency regularisation and 2. pseudo-labelling.

semi-supervised learning   computer vision   deep learning

The convex hull is a very important concept in geometry, and has many applications in fields such as computer vision, mathematics, statistics, and economics. Essentially, a convex hull of a shape or set of points is the smallest convex set that contains that shape or set of points. Many algorithms exist to compute a convex hull. Many of these algorithms have focused on the 2D or 3D case, however, the general $d$-dimensional case is of big interest in many applications.

computational geometry   geometry   computer vision

## Representation Learning (1)

For a while I’ve been interested in representation learning in the context of deep learning. Concepts such as self-supervised learning, unsupervised representation learning using GANs or VAEs, or simply through a vanilla supervised learning of some neural network architecture. Upon reading the literature, I had an idea that serves as a nice integration of two very interesting and useful models / techniques - the Fisher vector (which I’ve previously posted about in my blog here), and the variational autoencoder (which I’ve been meaning to write a blog post about!). This blog post just serves to flesh out the idea, should I choose to pursue or revisit it at some point.

representation learning   fisher vectors   deep learning

## SVMs: A Geometric Interpretation

support vector machine   svm   machine learning

## Human Action Recognition

In this post we will discuss the problem of human action recognition - an application of video analysis / recognition. The task is simply to identify a single action from a video. The typically setting is a dataset consisting of $N$ action classes, where each class has a set of videos associated with it relating to that action. We will focus on the approaches typically taken in early action recognition research, and then focus on the current state-of-the-art approaches. There is a recurring theme in action recognition of extending conventional two-dimensional algorithms into three dimensions to accommodate for the extra (temporal) dimension when dealing with videos instead of images.

cnn   deep learning   action recognition

## Dimensionality Reduction

In machine learning, we often work with very high-dimensional data. For example, we might be working in a genome prediction context, in which case our feature vectors would contains thousands of dimensions, or perhaps we’re dealing in another context where the dimensions reach of hundreds of thousands or possibly millions. In such a context, one common way to get a handle on the data - to understand it better - is to visualise the data by reducing its dimensions. The can be done using conventional dimensionality reduction techniques such as PCA and LDA, or using manifold learning techniques such as t-SNE and LLE.

dimensionality reduction   pca   t-SNE   machine learning   manifold learning

## Optical Flow

Optical flow is a method for motion analysis and image registration that aims to compute displacement of intensity patterns. Optical flow is used in many different settings in the computer vision realm, such as video recognition and video compression. The key assumption to many optical flow algorithms is known as the brightness constancy constraint, as is defined as:

optical flow   lucas kanade   dense optical flow   computer vision

## Ensemble Learning

Ensemble learning is one of the most useful methods in the machine learning, not least for the fact that it is essentially agnostic to the statistical learning algorithm being used. Ensemble learning techniques are a set of algorithms that define how to combine multiple classifiers to make one strong classifier. There are various ensemble learning techniques, but this post will focus on the two most popular - bagging and boosting. These two approach the same problem in very different ways.

ensemble learning   boosting   bagging   machine learning

## Autoencoders

Autoencoders fall under the unsupervised learning category, and are a special case of neural networks that map the inputs (in the input layer) back to the inputs (in the final layer). This can be seen mathematically as $f : \mathbb{R}^m \mapsto \mathbb{R}^m$. Autoencoders were originally introduced to address dimensionality reduction. In the original paper, Hinton compares it with PCA, another dimensionality reduction algorithm. He showed that autoencoders outperform PCA when non-linear mappings are needed to represent the data. They are able to learn a more realistic low-dimensional manifold than linear methods due to their non-linear nature.

autoencoder   auto-encoders   neural networks   deep learning   dimensionality reduction

## Face Recognition: Eigenfaces

The main idea behind eigenfaces is that we want to learn a low-dimensional space - known as the eigenface subspace - on which we assume the faces intrinsically lie. From there, we can then compare faces within this low-dimensional space in order to perform facial recognition. It’s a relatively simple approach to facial recognition, but indeed one of the most famous and effective ones of the early approaches. It still works well in simple, controlled scenarios.

face recognition   eigenfaces   pca

## Support Vector Machines - Why and How

Support vector machines (SVMs) are one of the most popular supervised learning algorithms in use today, even with the onslaught of deep learning and neural network take-over. The reason they have remained popular is due to their reliability across a wide variety of problem domains and datasets. They often have great generalisation performance, and this is almost solely due to the clever way in which they work - that is, how they approach the problem of supervised learning and how they formulate the optimisation problem they solve.

svm   support vector machine   machine learning   kernel machines

## Local Feature Encoding and Quantisation

In this post, I will describe local feature encoding and quantisation - why it is useful, where it is used, and some of the popular techniques used to perform it.

fisher vector   vlad   feature vectors   feature encoding   computer vision