9,509 research outputs found
Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification
Kernel methods have been successfully applied to the areas of pattern
recognition and data mining. In this paper, we mainly discuss the issue of
propagating labels in kernel space. A Kernel-Induced Label Propagation
(Kernel-LP) framework by mapping is proposed for high-dimensional data
classification using the most informative patterns of data in kernel space. The
essence of Kernel-LP is to perform joint label propagation and adaptive weight
learning in a transformed kernel space. That is, our Kernel-LP changes the task
of label propagation from the commonly-used Euclidean space in most existing
work to kernel space. The motivation of our Kernel-LP to propagate labels and
learn the adaptive weights jointly by the assumption of an inner product space
of inputs, i.e., the original linearly inseparable inputs may be mapped to be
separable in kernel space. Kernel-LP is based on existing positive and negative
LP model, i.e., the effects of negative label information are integrated to
improve the label prediction power. Also, Kernel-LP performs adaptive weight
construction over the same kernel space, so it can avoid the tricky process of
choosing the optimal neighborhood size suffered in traditional criteria. Two
novel and efficient out-of-sample approaches for our Kernel-LP to involve new
test data are also presented, i.e., (1) direct kernel mapping and (2) kernel
mapping-induced label reconstruction, both of which purely depend on the kernel
matrix between training set and testing set. Owing to the kernel trick, our
algorithms will be applicable to handle the high-dimensional real data.
Extensive results on real datasets demonstrate the effectiveness of our
approach.Comment: Accepted by IEEE TB
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
We consider the problem of zero-shot recognition: learning a visual
classifier for a category with zero training examples, just using the word
embedding of the category and its relationship to other categories, which
visual data are provided. The key to dealing with the unfamiliar or novel
category is to transfer knowledge obtained from familiar classes to describe
the unfamiliar class. In this paper, we build upon the recently introduced
Graph Convolutional Network (GCN) and propose an approach that uses both
semantic embeddings and the categorical relationships to predict the
classifiers. Given a learned knowledge graph (KG), our approach takes as input
semantic embeddings for each node (representing visual category). After a
series of graph convolutions, we predict the visual classifier for each
category. During training, the visual classifiers for a few categories are
given to learn the GCN parameters. At test time, these filters are used to
predict the visual classifiers of unseen categories. We show that our approach
is robust to noise in the KG. More importantly, our approach provides
significant improvement in performance compared to the current state-of-the-art
results (from 2 ~ 3% on some metrics to whopping 20% on a few).Comment: CVPR 201
Regression-based Hypergraph Learning for Image Clustering and Classification
Inspired by the recently remarkable successes of Sparse Representation (SR),
Collaborative Representation (CR) and sparse graph, we present a novel
hypergraph model named Regression-based Hypergraph (RH) which utilizes the
regression models to construct the high quality hypergraphs. Moreover, we plug
RH into two conventional hypergraph learning frameworks, namely hypergraph
spectral clustering and hypergraph transduction, to present Regression-based
Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph
Transduction (RHT) models for addressing the image clustering and
classification issues. Sparse Representation and Collaborative Representation
are employed to instantiate two RH instances and their RHSC and RHT algorithms.
The experimental results on six popular image databases demonstrate that the
proposed RH learning algorithms achieve promising image clustering and
classification performances, and also validate that RH can inherit the
desirable properties from both hypergraph models and regression models.Comment: 11page
Autoencoder Based Sample Selection for Self-Taught Learning
Self-taught learning is a technique that uses a large number of unlabeled
data as source samples to improve the task performance on target samples.
Compared with other transfer learning techniques, self-taught learning can be
applied to a broader set of scenarios due to the loose restrictions on the
source data. However, knowledge transferred from source samples that are not
sufficiently related to the target domain may negatively influence the target
learner, which is referred to as negative transfer. In this paper, we propose a
metric for the relevance between a source sample and the target samples. To be
more specific, both source and target samples are reconstructed through a
single-layer autoencoder with a linear relationship between source samples and
reconstructed target samples being simultaneously enforced. An
-norm sparsity constraint is imposed on the transformation matrix
to identify source samples relevant to the target domain. Source domain samples
that are deemed relevant are assigned pseudo-labels reflecting their relevance
to target domain samples, and are combined with target samples in order to
provide an expanded training set for classifier training. Local data structures
are also preserved during source sample selection through spectral graph
analysis. Promising results in extensive experiments show the advantages of the
proposed approach.Comment: 38 pages, 4 figures, to appear in Elsevier Knowledge-Based System
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Face recognition has witnessed great progress in recent years, mainly
attributed to the high-capacity model designed and the abundant labeled data
collected. However, it becomes more and more prohibitive to scale up the
current million-level identity annotations. In this work, we show that
unlabeled face data can be as effective as the labeled ones. Here, we consider
a setting closely mimicking the real-world scenario, where the unlabeled data
are collected from unconstrained environments and their identities are
exclusive from the labeled ones. Our main insight is that although the class
information is not available, we can still faithfully approximate these
semantic relationships by constructing a relational graph in a bottom-up
manner. We propose Consensus-Driven Propagation (CDP) to tackle this
challenging problem with two modules, the "committee" and the "mediator", which
select positive face pairs robustly by carefully aggregating multi-view
information. Extensive experiments validate the effectiveness of both modules
to discard outliers and mine hard positives. With CDP, we achieve a compelling
accuracy of 78.18% on MegaFace identification challenge by using only 9% of the
labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all
labels are employed.Comment: In ECCV 2018. More details at the project page:
http://mmlab.ie.cuhk.edu.hk/projects/CDP
Domain Adaptation with Adversarial Training and Graph Embeddings
The success of deep neural networks (DNNs) is heavily dependent on the
availability of labeled data. However, obtaining labeled data is a big
challenge in many real-world problems. In such scenarios, a DNN model can
leverage labeled and unlabeled data from a related domain, but it has to deal
with the shift in data distributions between the source and the target domains.
In this paper, we study the problem of classifying social media posts during a
crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data
from past similar events (e.g., Flood) and unlabeled data for the current
event. We propose a novel model that performs adversarial learning based domain
adaptation to deal with distribution drifts and graph based semi-supervised
learning to leverage unlabeled data within a single unified deep learning
framework. Our experiments with two real-world crisis datasets collected from
Twitter demonstrate significant improvements over several baselines.Comment: This is a pre-print of our paper accepted to appear in the
proceedings of the ACL, 201
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
Morpho-syntactic lexicons provide information about the morphological and
syntactic roles of words in a language. Such lexicons are not available for all
languages and even when available, their coverage can be limited. We present a
graph-based semi-supervised learning method that uses the morphological,
syntactic and semantic relations between words to automatically construct wide
coverage lexicons from small seed sets. Our method is language-independent, and
we show that we can expand a 1000 word seed lexicon to more than 100 times its
size with high quality for 11 languages. In addition, the automatically created
lexicons provide features that improve performance in two downstream tasks:
morphological tagging and dependency parsing.Comment: Transactions of the Association for Computational Linguistics (TACL)
201
Visual Tracking via Dynamic Graph Learning
Existing visual tracking methods usually localize a target object with a
bounding box, in which the performance of the foreground object trackers or
detectors is often affected by the inclusion of background clutter. To handle
this problem, we learn a patch-based graph representation for visual tracking.
The tracked object is modeled by with a graph by taking a set of
non-overlapping image patches as nodes, in which the weight of each node
indicates how likely it belongs to the foreground and edges are weighted for
indicating the appearance compatibility of two neighboring nodes. This graph is
dynamically learned and applied in object tracking and model updating. During
the tracking process, the proposed algorithm performs three main steps in each
frame. First, the graph is initialized by assigning binary weights of some
image patches to indicate the object and background patches according to the
predicted bounding box. Second, the graph is optimized to refine the patch
weights by using a novel alternating direction method of multipliers. Third,
the object feature representation is updated by imposing the weights of patches
on the extracted image features. The object location is predicted by maximizing
the classification score in the structured support vector machine. Extensive
experiments show that the proposed tracking algorithm performs well against the
state-of-the-art methods on large-scale benchmark datasets.Comment: Submitted to TPAMI 201
Robust Semi-Supervised Classification for Multi-Relational Graphs
Graph-regularized semi-supervised learning has been used effectively for
classification when (i) instances are connected through a graph, and (ii)
labeled data is scarce. If available, using multiple relations (or graphs)
between the instances can improve the prediction performance. On the other
hand, when these relations have varying levels of veracity and exhibit varying
relevance for the task, very noisy and/or irrelevant relations may deteriorate
the performance. As a result, an effective weighing scheme needs to be put in
place. In this work, we propose a robust and scalable approach for
multi-relational graph-regularized semi-supervised classification. Under a
convex optimization scheme, we simultaneously infer weights for the multiple
graphs as well as a solution. We provide a careful analysis of the inferred
weights, based on which we devise an algorithm that filters out irrelevant and
noisy graphs and produces weights proportional to the informativeness of the
remaining graphs. Moreover, the proposed method is linearly scalable w.r.t. the
number of edges in the union of the multiple graphs. Through extensive
experiments we show that our method yields superior results under different
noise models, and under increasing number of noisy graphs and intensity of
noise, as compared to a list of baselines and state-of-the-art approaches.Comment: 14 pages, 8 figures, 3 table
GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models
Modern data analysis pipelines are becoming increasingly complex due to the
presence of multi-view information sources. While graphs are effective in
modeling complex relationships, in many scenarios a single graph is rarely
sufficient to succinctly represent all interactions, and hence multi-layered
graphs have become popular. Though this leads to richer representations,
extending solutions from the single-graph case is not straightforward.
Consequently, there is a strong need for novel solutions to solve classical
problems, such as node classification, in the multi-layered case. In this
paper, we consider the problem of semi-supervised learning with multi-layered
graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for
community discovery, we argue that feature learning with random node
attributes, using graph neural networks, can be more effective. To this end, we
propose to use attention models for effective feature learning, and develop two
novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer
dependencies for building multi-layered graph embeddings. Using empirical
studies on several benchmark datasets, we evaluate the proposed approaches and
demonstrate significant performance improvements in comparison to
state-of-the-art network embedding strategies. The results also show that using
simple random features is an effective choice, even in cases where explicit
node attributes are not available
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