21,784 research outputs found
Spectral Analysis Network for Deep Representation Learning and Image Clustering
Deep representation learning is a crucial procedure in multimedia analysis
and attracts increasing attention. Most of the popular techniques rely on
convolutional neural network and require a large amount of labeled data in the
training procedure. However, it is time consuming or even impossible to obtain
the label information in some tasks due to cost limitation. Thus, it is
necessary to develop unsupervised deep representation learning techniques. This
paper proposes a new network structure for unsupervised deep representation
learning based on spectral analysis, which is a popular technique with solid
theory foundations. Compared with the existing spectral analysis methods, the
proposed network structure has at least three advantages. Firstly, it can
identify the local similarities among images in patch level and thus more
robust against occlusion. Secondly, through multiple consecutive spectral
analysis procedures, the proposed network can learn more clustering-friendly
representations and is capable to reveal the deep correlations among data
samples. Thirdly, it can elegantly integrate different spectral analysis
procedures, so that each spectral analysis procedure can have their individual
strengths in dealing with different data sample distributions. Extensive
experimental results show the effectiveness of the proposed methods on various
image clustering tasks
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an
effective approach, especially by using nuclear medicine imaging techniques
such as Positron Emission Topography (PET). In various literature it has been
found that PET images can be better modeled as signals (e.g. uptake of
florbetapir) defined on a network (non-Euclidean) structure which is governed
by its underlying graph patterns of pathological progression and metabolic
connectivity. In order to effectively apply deep learning framework for PET
image analysis to overcome its limitation on Euclidean grid, we develop a
solution for 3D PET image representation and analysis under a generalized,
graph-based CNN architecture (PETNet), which analyzes PET signals defined on a
group-wise inferred graph structure. Computations in PETNet are defined in
non-Euclidean, graph (network) domain, as it performs feature extraction by
convolution operations on spectral-filtered signals on the graph and pooling
operations based on hierarchical graph clustering. Effectiveness of the PETNet
is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset,
which shows improved performance over both deep learning and other machine
learning-based methods.Comment: Jiaming Guo, Wei Qiu and Xiang Li contribute equally to this wor
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
- …