2,347 research outputs found
AffinityNet: semi-supervised few-shot learning for disease type prediction
While deep learning has achieved great success in computer vision and many
other fields, currently it does not work very well on patient genomic data with
the "big p, small N" problem (i.e., a relatively small number of samples with
high-dimensional features). In order to make deep learning work with a small
amount of training data, we have to design new models that facilitate few-shot
learning. Here we present the Affinity Network Model (AffinityNet), a data
efficient deep learning model that can learn from a limited number of training
examples and generalize well. The backbone of the AffinityNet model consists of
stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention
pooling layer is a generalization of the Graph Attention Model (GAM), and can
be applied to not only graphs but also any set of objects regardless of whether
a graph is given or not. As a new deep learning module, kNN attention pooling
layers can be plugged into any neural network model just like convolutional
layers. As a simple special case of kNN attention pooling layer, feature
attention layer can directly select important features that are useful for
classification tasks. Experiments on both synthetic data and cancer genomic
data from TCGA projects show that our AffinityNet model has better
generalization power than conventional neural network models with little
training data. The code is freely available at
https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure
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
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
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