Abstract: The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
Abstract: With increasing data in medical databases, medical
data retrieval is growing in popularity. Some of this analysis
including inducing propositional rules from databases using many
soft techniques, and then using these rules in an expert system.
Diagnostic rules and information on features are extracted from
clinical databases on diseases of congenital anomaly. This paper
explain the latest soft computing techniques and some of the
adaptive techniques encompasses an extensive group of methods
that have been applied in the medical domain and that are used for
the discovery of data dependencies, importance of features,
patterns in sample data, and feature space dimensionality
reduction. These approaches pave the way for new and interesting
avenues of research in medical imaging and represent an important
challenge for researchers.
Abstract: Sparse representation which can represent high dimensional
data effectively has been successfully used in computer vision
and pattern recognition problems. However, it doesn-t consider the
label information of data samples. To overcome this limitation,
we develop a novel dimensionality reduction algorithm namely
dscriminatively regularized sparse subspace learning(DR-SSL) in this
paper. The proposed DR-SSL algorithm can not only make use of
the sparse representation to model the data, but also can effective
employ the label information to guide the procedure of dimensionality
reduction. In addition,the presented algorithm can effectively deal
with the out-of-sample problem.The experiments on gene-expression
data sets show that the proposed algorithm is an effective tool for
dimensionality reduction and gene-expression data classification.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.