408 research outputs found
Non-negative matrix factorization with sparseness constraints
Non-negative matrix factorization (NMF) is a recently developed technique for
finding parts-based, linear representations of non-negative data. Although it
has successfully been applied in several applications, it does not always
result in parts-based representations. In this paper, we show how explicitly
incorporating the notion of `sparseness' improves the found decompositions.
Additionally, we provide complete MATLAB code both for standard NMF and for our
extension. Our hope is that this will further the application of these methods
to solving novel data-analysis problems
Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization
Nonnegative matrix factorization (NMF) has been successfully applied to many
areas for classification and clustering. Commonly-used NMF algorithms mainly
target on minimizing the distance or Kullback-Leibler (KL) divergence,
which may not be suitable for nonlinear case. In this paper, we propose a new
decomposition method by maximizing the correntropy between the original and the
product of two low-rank matrices for document clustering. This method also
allows us to learn the new basis vectors of the semantic feature space from the
data. To our knowledge, we haven't seen any work has been done by maximizing
correntropy in NMF to cluster high dimensional document data. Our experiment
results show the supremacy of our proposed method over other variants of NMF
algorithm on Reuters21578 and TDT2 databasets.Comment: International Conference of Machine Learning and Cybernetics (ICMLC)
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Using machine learning on the sources of retinal images for diagnosis by proxy of diabetes mellitus and diabetic retinopathy
In current research in ophthalmology, images of the vascular system in the human retina are used as exploratory proxies for pathologies affecting different organs. This thesis addresses the analysis, using machine learning and computer vision techniques, of retinal images acquired with different techniques (Fundus retinographies, optical coherence tomography and optical coherence tomography angiography), with the objective of using them to assist diagnostic decision making in diabetes mellitus and diabetic retinopathy. This thesis explores the use of matrix factorization-based source extraction techniques, as the basis to transform the retinal images for classification. The proposed approach consists on preprocessing the images to enable the learning of an unsupervised parts-based representation prior to the classification. As a result of the use of interpretable models, with this approach we unveiled an important bias in the data. After correcting for the bias, promising results were still obtained which merit for further exploration
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many
clustering and classification tasks. The classic ways to measure the errors
between the original and the reconstructed matrix are distance or
Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly
handled when we use these error measures. As a consequence, alternative
measures based on nonlinear kernels, such as correntropy, are proposed.
However, the current correntropy-based NMF only targets on the low-level
features without considering the intrinsic geometrical distribution of data. In
this paper, we propose a new NMF algorithm that preserves local invariance by
adding graph regularization into the process of max-correntropy-based matrix
factorization. Meanwhile, each feature can learn corresponding kernel from the
data. The experiment results of Caltech101 and Caltech256 show the benefits of
such combination against other NMF algorithms for the unsupervised image
clustering
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Depth-adaptive methodologies for 3D image caregorization.
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Image classification is an active topic of computer vision research. This topic
deals with the learning of patterns in order to allow efficient classification of visual
information. However, most research efforts have focused on 2D image classification.
In recent years, advances of 3D imaging enabled the development of applications and
provided new research directions. In this thesis, we present methodologies and techniques for image classification using 3D image data. We conducted our research focusing on the attributes and
limitations of depth information regarding possible uses. This research led us to the
development of depth feature extraction methodologies that contribute to the representation
of images thus enhancing the recognition efficiency. We proposed a new
classification algorithm that adapts to the need of image representations by implementing
a scale-based decision that exploits discriminant parts of representations.
Learning from the design of image representation methods, we introduced our own
which describes each image by its depicting content providing more discriminative image
representation. We also propose a dictionary learning method that exploits the
relation of training features by assessing the similarity of features originating from
similar context regions. Finally, we present our research on deep learning algorithms
combined with data and techniques used in 3D imaging. Our novel methods provide
state-of-the-art results, thus contributing to the research of 3D image classificatio
Object Recognition
Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs
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