26,160 research outputs found
A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis
Prediction problems from spectra are largely encountered in chemometry. In
addition to accurate predictions, it is often needed to extract information
about which wavelengths in the spectra contribute in an effective way to the
quality of the prediction. This implies to select wavelengths (or wavelength
intervals), a problem associated to variable selection. In this paper, it is
shown how this problem may be tackled in the specific case of smooth (for
example infrared) spectra. The functional character of the spectra (their
smoothness) is taken into account through a functional variable projection
procedure. Contrarily to standard approaches, the projection is performed on a
basis that is driven by the spectra themselves, in order to best fit their
characteristics. The methodology is illustrated by two examples of functional
projection, using Independent Component Analysis and functional variable
clustering, respectively. The performances on two standard infrared spectra
benchmarks are illustrated.Comment: A paraitr
On Practical machine Learning and Data Analysis
This thesis discusses and addresses some of the difficulties
associated with practical machine learning and data
analysis. Introducing data driven methods in e.g industrial and
business applications can lead to large gains in productivity and
efficiency, but the cost and complexity are often
overwhelming. Creating machine learning applications in practise often
involves a large amount of manual labour, which often needs to be
performed by an experienced analyst without significant experience
with the application area. We will here discuss some of the hurdles
faced in a typical analysis project and suggest measures and methods
to simplify the process.
One of the most important issues when applying machine learning
methods to complex data, such as e.g. industrial applications, is that
the processes generating the data are modelled in an appropriate
way. Relevant aspects have to be formalised and represented in a way
that allow us to perform our calculations in an efficient manner. We
present a statistical modelling framework, Hierarchical Graph
Mixtures, based on a combination of graphical models and mixture
models. It allows us to create consistent, expressive statistical
models that simplify the modelling of complex systems. Using a
Bayesian approach, we allow for encoding of prior knowledge and make
the models applicable in situations when relatively little data are
available.
Detecting structures in data, such as clusters and dependency
structure, is very important both for understanding an application
area and for specifying the structure of e.g. a hierarchical graph
mixture. We will discuss how this structure can be extracted for
sequential data. By using the inherent dependency structure of
sequential data we construct an information theoretical measure of
correlation that does not suffer from the problems most common
correlation measures have with this type of data.
In many diagnosis situations it is desirable to perform a
classification in an iterative and interactive manner. The matter is
often complicated by very limited amounts of knowledge and examples
when a new system to be diagnosed is initially brought into use. We
describe how to create an incremental classification system based on a
statistical model that is trained from empirical data, and show how
the limited available background information can still be used
initially for a functioning diagnosis system.
To minimise the effort with which results are achieved within data
analysis projects, we need to address not only the models used, but
also the methodology and applications that can help simplify the
process. We present a methodology for data preparation and a software
library intended for rapid analysis, prototyping, and deployment.
Finally, we will study a few example applications, presenting tasks
within classification, prediction and anomaly detection. The examples
include demand prediction for supply chain management, approximating
complex simulators for increased speed in parameter optimisation, and
fraud detection and classification within a media-on-demand system
Kernel Spectral Clustering and applications
In this chapter we review the main literature related to kernel spectral
clustering (KSC), an approach to clustering cast within a kernel-based
optimization setting. KSC represents a least-squares support vector machine
based formulation of spectral clustering described by a weighted kernel PCA
objective. Just as in the classifier case, the binary clustering model is
expressed by a hyperplane in a high dimensional space induced by a kernel. In
addition, the multi-way clustering can be obtained by combining a set of binary
decision functions via an Error Correcting Output Codes (ECOC) encoding scheme.
Because of its model-based nature, the KSC method encompasses three main steps:
training, validation, testing. In the validation stage model selection is
performed to obtain tuning parameters, like the number of clusters present in
the data. This is a major advantage compared to classical spectral clustering
where the determination of the clustering parameters is unclear and relies on
heuristics. Once a KSC model is trained on a small subset of the entire data,
it is able to generalize well to unseen test points. Beyond the basic
formulation, sparse KSC algorithms based on the Incomplete Cholesky
Decomposition (ICD) and , , Group Lasso regularization are
reviewed. In that respect, we show how it is possible to handle large scale
data. Also, two possible ways to perform hierarchical clustering and a soft
clustering method are presented. Finally, real-world applications such as image
segmentation, power load time-series clustering, document clustering and big
data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms
Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach
Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy
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