UH Biocomputation Group - PCA

Asynchronism-based principal component analysis for time series data mining

2018-10-10T14:33:32+01:00

Yi Sun's journal club session, where she will present the paper "Asynchronism-based principal component analysis for time series data mining (Hailin Li, 2014)".

Principal component analysis (PCA) is often applied to dimensionality reduction for time series data mining. However, the principle of PCA is based on the synchronous covariance, which is not very effective in some cases. In this paper, an asynchronism-based principal component analysis (APCA) is proposed to reduce the dimensionality of univariate time series. In the process of APCA, an asynchronous method based on dynamic time warping (DTW) is developed to obtain the interpolated time series which derive from the original ones. The correlation coefficient or covariance between the interpolated time series represents the correlation between the original ones. In this way, a novel and valid principal component analysis based on the asynchronous covariance is achieved to reduce the dimensionality. The results of several experiments demonstrate that the proposed approach APCA outperforms PCA for dimensionality reduction in the field of time series data mining.

Date: 12/10/2018
Time: 16:00
Location: LB252

Detecting emotion of a person from their voice

2017-03-02T15:03:38+00:00

I will discuss the importance of recognising the emotional state of a person from the tone of their voice. For example, information may be conveyed by the pitch (frequency), tone, and volume of a speaker's voice.

I am interested in investigating how a machine learning method may be able to discriminate between voices conveying varying emotions. I will show how unsupervised techniques (such as PCA) and supervised techniques, such as SVMs, may be able to tackle this problem.

I intend my contribution to existing research will be in the area of robustness and accuracy.

Date: 3/03/2017
Time: 16:00
Location: LB252

Dynamic functional principal components

2016-11-29T10:43:11+00:00

In this presentation, I shall talk about S. Hofmann and L. Kidzinski's research paper, entitled Dynamic functional principal components. The paper has been published in Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 77, Issue 2, March 2015, Pages 319–348: http://onlinelibrary.wiley.com/doi/10.1111/rssb.12076/full

I shall introduce some basic background knowledge rather than discussing mathematical equations in the paper. Especially, I shall talk about the idea of Functional Principal Components, of Dynamic Principal Components, and how these two methods are combined together to be used in analysing time series data in this paper.

Date: 02/12/2016
Time: 16:00
Location: LB252

Principal component analysis

2016-10-20T13:30:55+01:00

Principle Component Analysis (PCA) is used by many but understood by few.

In this talk I hope to explain some of the math behind the tool, to raise awareness for its potential (and pitfalls) and to discuss the relationship between PCA and cluster analysis. In addition I will present some examples of its use in neuro-science, from my own work in robotics and an application in economics.

Date: 21/10/2016
Time: 16:00
Location: LB252