High Dimensional Data
high dimensional data
Maths, AI and Neuroscience Meeting Stockholm
To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent.
Maths, AI and Neuroscience meeting
To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent. In this meeting we bring together experts from Mathematics, Artificial Intelligence and Neuroscience for a three day long hybrid meeting. We will have talks on mathematical tools in particular Topology to understand high dimensional data, explainable AI, how AI can help neuroscience and to what extent the brain may be using algorithms similar to the ones used in modern machine learning. Finally we will wrap up with a discussion on some aspects of neural hardware that may not have been considered in machine learning.
NMC4 Short Talk: Rank similarity filters for computationally-efficient machine learning on high dimensional data
Real world datasets commonly contain nonlinearly separable classes, requiring nonlinear classifiers. However, these classifiers are less computationally efficient than their linear counterparts. This inefficiency wastes energy, resources and time. We were inspired by the efficiency of the brain to create a novel type of computationally efficient Artificial Neural Network (ANN) called Rank Similarity Filters. They can be used to both transform and classify nonlinearly separable datasets with many datapoints and dimensions. The weights of the filters are set using the rank orders of features in a datapoint, or optionally the 'confusion' adjusted ranks between features (determined from their distributions in the dataset). The activation strength of a filter determines its similarity to other points in the dataset, a measure based on cosine similarity. The activation of many Rank Similarity Filters transforms samples into a new nonlinear space suitable for linear classification (Rank Similarity Transform (RST)). We additionally used this method to create the nonlinear Rank Similarity Classifier (RSC), which is a fast and accurate multiclass classifier, and the nonlinear Rank Similarity Probabilistic Classifier (RSPC), which is an extension to the multilabel case. We evaluated the classifiers on multiple datasets and RSC is competitive with existing classifiers but with superior computational efficiency. Code for RST, RSC and RSPC is open source and was written in Python using the popular scikit-learn framework to make it easily accessible (https://github.com/KatharineShapcott/rank-similarity). In future extensions the algorithm can be applied to hardware suitable for the parallelization of an ANN (GPU) and a Spiking Neural Network (neuromorphic computing) with corresponding performance gains. This makes Rank Similarity Filters a promising biologically inspired solution to the problem of efficient analysis of nonlinearly separable data.