2,756 research outputs found
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks
Deep clustering has recently emerged as a promising technique for complex
data clustering. Despite the considerable progress, previous deep clustering
works mostly build or learn the final clustering by only utilizing a single
layer of representation, e.g., by performing the K-means clustering on the last
fully-connected layer or by associating some clustering loss to a specific
layer, which neglect the possibilities of jointly leveraging multi-layer
representations for enhancing the deep clustering performance. In view of this,
this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which
bridges the gap between deep clustering and ensemble clustering by harnessing
the power of multiple layers in deep neural networks. In particular, we utilize
a weight-sharing convolutional neural network as the backbone, which is trained
with both the instance-level contrastive learning (via an instance projector)
and the cluster-level contrastive learning (via a cluster projector) in an
unsupervised manner. Thereafter, multiple layers of feature representations are
extracted from the trained network, upon which the ensemble clustering process
is further conducted. Specifically, a set of diversified base clusterings are
generated from the multi-layer representations via a highly efficient
clusterer. Then the reliability of clusters in multiple base clusterings is
automatically estimated by exploiting an entropy-based criterion, based on
which the set of base clusterings are re-formulated into a weighted-cluster
bipartite graph. By partitioning this bipartite graph via transfer cut, the
final consensus clustering can be obtained. Experimental results on six image
datasets confirm the advantages of DeepCluE over the state-of-the-art deep
clustering approaches.Comment: To appear in IEEE Transactions on Emerging Topics in Computational
Intelligenc
Coupled Maps with Growth and Death: An Approach to Cell Differentiation
An extension of coupled maps is given which allows for the growth of the
number of elements, and is inspired by the cell differentiation problem. The
growth of elements is made possible first by clustering the phases, and then by
differentiating roles. The former leads to the time sharing of resources, while
the latter leads to the separation of roles for the growth. The mechanism of
the differentiation of elements is studied. An extension to a model with
several internal phase variables is given, which shows differentiation of
internal states. The relevance of interacting dynamics with internal states
(``intra-inter" dynamics) to biological problems is discussed with an emphasis
on heterogeneity by clustering, macroscopic robustness by partial
synchronization and recursivity with the selection of initial conditions and
digitalization.Comment: LatexText,figures are not included. submitted to PhysicaD
(1995,revised 1996 May
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
Recommended from our members
Prediction of microbial communities for urban metagenomics using neural network approach.
BACKGROUND:Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS:We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations
Class-Decomposition and Augmentation for Imbalanced Data Sentiment Analysis
Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's bias toward the majority class instances. In this paper, we present a new approach to handle class-imbalance in text data by means of unsupervised learning algorithms. We present class-decomposition using two different unsupervised methods, namely k-means and Density-Based Spatial Clustering of Applications with Noise, applied to two different sentiment analysis data sets. The experimental results show that utilizing clustering to find within-class similarities can lead to significant improvement in learning algorithm's performances as well as reducing the dominance of the majority class instances without causing information loss
- …