8,586 research outputs found
The intrinsic value of HFO features as a biomarker of epileptic activity
High frequency oscillations (HFOs) are a promising biomarker of epileptic
brain tissue and activity. HFOs additionally serve as a prototypical example of
challenges in the analysis of discrete events in high-temporal resolution,
intracranial EEG data. Two primary challenges are 1) dimensionality reduction,
and 2) assessing feasibility of classification. Dimensionality reduction
assumes that the data lie on a manifold with dimension less than that of the
feature space. However, previous HFO analyses have assumed a linear manifold,
global across time, space (i.e. recording electrode/channel), and individual
patients. Instead, we assess both a) whether linear methods are appropriate and
b) the consistency of the manifold across time, space, and patients. We also
estimate bounds on the Bayes classification error to quantify the distinction
between two classes of HFOs (those occurring during seizures and those
occurring due to other processes). This analysis provides the foundation for
future clinical use of HFO features and buides the analysis for other discrete
events, such as individual action potentials or multi-unit activity.Comment: 5 pages, 5 figure
Dimension Estimation Using Random Connection Models
Information about intrinsic dimension is crucial to perform dimensionality
reduction, compress information, design efficient algorithms, and do
statistical adaptation. In this paper we propose an estimator for the intrinsic
dimension of a data set. The estimator is based on binary neighbourhood
information about the observations in the form of two adjacency matrices, and
does not require any explicit distance information. The underlying graph is
modelled according to a subset of a specific random connection model, sometimes
referred to as the Poisson blob model. Computationally the estimator scales
like n log n, and we specify its asymptotic distribution and rate of
convergence. A simulation study on both real and simulated data shows that our
approach compares favourably with some competing methods from the literature,
including approaches that rely on distance information
Design and Evaluation of a Probabilistic Music Projection Interface
We describe the design and evaluation of a probabilistic
interface for music exploration and casual playlist generation.
Predicted subjective features, such as mood and
genre, inferred from low-level audio features create a 34-
dimensional feature space. We use a nonlinear dimensionality
reduction algorithm to create 2D music maps of
tracks, and augment these with visualisations of probabilistic
mappings of selected features and their uncertainty.
We evaluated the system in a longitudinal trial in users’
homes over several weeks. Users said they had fun with the
interface and liked the casual nature of the playlist generation.
Users preferred to generate playlists from a local
neighbourhood of the map, rather than from a trajectory,
using neighbourhood selection more than three times more
often than path selection. Probabilistic highlighting of subjective
features led to more focused exploration in mouse
activity logs, and 6 of 8 users said they preferred the probabilistic
highlighting mode
Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/ licenses/by/4.0
Statistical estimation of the intrinsic dimensionality of data collections
A realization fi(·) from a class F(·) can be represented as a point in a metric space and the locus of all points belonging to F(·) lie on a surface in this space. The intrinsic dimensionality of F(·), defined as the least number of parameters needed to identify any fi(·) belonging to F(·), is equal to the topological dimensionality of this surface. Given a sample set of realizations fi(·) from F(·), a statistical method is presented for estimating the intrinsic dimensionality of F(·)
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