Authors:
Alon Schclar
1
and
Amir Averbuch
2
Affiliations:
1
The Academic College of Tel-Aviv Yaffo, Israel
;
2
Tel-Aviv University, Israel
Keyword(s):
Dimensionality Reduction, Unsupervised Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
The overflow of data is a critical contemporary challenge in many areas such as hyper-spectral sensing, information
retrieval, biotechnology, social media mining, classification etc. It is usually manifested by a high-dimensional representation of data observations. In most cases, the information that is inherent in highdimensional
datasets is conveyed by a small number of parameters that correspond to the actual degrees of
freedom of the dataset. In order to efficiently process the dataset, one needs to derive these parameters by
embedding the dataset into a low-dimensional space. This process is commonly referred to as dimensionality
reduction or feature extraction. We present a novel algorithm for dimensionality reduction – diffusion bases –
which explores the connectivity among the coordinates of the data and is dual to the diffusion maps algorithm.
The algorithm reduces the dimensionality of the data while maintaining the coherency of the information that
is conveyed by
the data.
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