14,860 research outputs found
Community Detection with and without Prior Information
We study the problem of graph partitioning, or clustering, in sparse networks
with prior information about the clusters. Specifically, we assume that for a
fraction of the nodes their true cluster assignments are known in
advance. This can be understood as a semi--supervised version of clustering, in
contrast to unsupervised clustering where the only available information is the
graph structure. In the unsupervised case, it is known that there is a
threshold of the inter--cluster connectivity beyond which clusters cannot be
detected. Here we study the impact of the prior information on the detection
threshold, and show that even minute [but generic] values of shift the
threshold downwards to its lowest possible value. For weighted graphs we show
that a small semi--supervising can be used for a non-trivial definition of
communities.Comment: 6 pages, 2 figure
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining
(SDM'18
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
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