4,413 research outputs found
Transfer Metric Learning: Algorithms, Applications and Outlooks
Distance metric learning (DML) aims to find an appropriate way to reveal the
underlying data relationship. It is critical in many machine learning, pattern
recognition and data mining algorithms, and usually require large amount of
label information (such as class labels or pair/triplet constraints) to achieve
satisfactory performance. However, the label information may be insufficient in
real-world applications due to the high-labeling cost, and DML may fail in this
case. Transfer metric learning (TML) is able to mitigate this issue for DML in
the domain of interest (target domain) by leveraging knowledge/information from
other related domains (source domains). Although achieved a certain level of
development, TML has limited success in various aspects such as selective
transfer, theoretical understanding, handling complex data, big data and
extreme cases. In this survey, we present a systematic review of the TML
literature. In particular, we group TML into different categories according to
different settings and metric transfer strategies, such as direct metric
approximation, subspace approximation, distance approximation, and distribution
approximation. A summarization and insightful discussion of the various TML
approaches and their applications will be presented. Finally, we indicate some
challenges and provide possible future directions.Comment: 14 pages, 5 figure
Transfer Adaptation Learning: A Decade Survey
The world we see is ever-changing and it always changes with people, things,
and the environment. Domain is referred to as the state of the world at a
certain moment. A research problem is characterized as transfer adaptation
learning (TAL) when it needs knowledge correspondence between different
moments/domains. Conventional machine learning aims to find a model with the
minimum expected risk on test data by minimizing the regularized empirical risk
on the training data, which, however, supposes that the training and test data
share similar joint probability distribution. TAL aims to build models that can
perform tasks of target domain by learning knowledge from a semantic related
but distribution different source domain. It is an energetic research filed of
increasing influence and importance, which is presenting a blowout publication
trend. This paper surveys the advances of TAL methodologies in the past decade,
and the technical challenges and essential problems of TAL have been observed
and discussed with deep insights and new perspectives. Broader solutions of
transfer adaptation learning being created by researchers are identified, i.e.,
instance re-weighting adaptation, feature adaptation, classifier adaptation,
deep network adaptation and adversarial adaptation, which are beyond the early
semi-supervised and unsupervised split. The survey helps researchers rapidly
but comprehensively understand and identify the research foundation, research
status, theoretical limitations, future challenges and under-studied issues
(universality, interpretability, and credibility) to be broken in the field
toward universal representation and safe applications in open-world scenarios.Comment: 26 pages, 4 figure
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
In this paper, we develop an approach to exploiting kernel methods with
manifold-valued data. In many computer vision problems, the data can be
naturally represented as points on a Riemannian manifold. Due to the
non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision
and machine learning algorithms yield inferior results on such data. In this
paper, we define Gaussian radial basis function (RBF)-based positive definite
kernels on manifolds that permit us to embed a given manifold with a
corresponding metric in a high dimensional reproducing kernel Hilbert space.
These kernels make it possible to utilize algorithms developed for linear
spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with
any given metric is not always positive definite, we present a unified
framework for analyzing the positive definiteness of the Gaussian RBF on a
generic metric space. We then use the proposed framework to identify positive
definite kernels on two specific manifolds commonly encountered in computer
vision: the Riemannian manifold of symmetric positive definite matrices and the
Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a
Euclidean space. We show that many popular algorithms designed for Euclidean
spaces, such as support vector machines, discriminant analysis and principal
component analysis can be generalized to Riemannian manifolds with the help of
such positive definite Gaussian kernels
Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond
The rapid emergence of high-dimensional data in various areas has brought new
challenges to current ensemble clustering research. To deal with the curse of
dimensionality, recently considerable efforts in ensemble clustering have been
made by incorporating different subspace-based techniques. However, besides the
emphasis on subspaces, rather limited attention has been paid to the potential
diversity in similarity/dissimilarity metrics. It remains a surprisingly open
problem in ensemble clustering how to create and aggregate a large population
of diversified metrics, and furthermore, how to jointly investigate the
multi-level diversity in the large populations of metrics, subspaces, and
clusters in a unified framework. To tackle this problem, this paper proposes a
novel multi-diversified ensemble clustering approach. In particular, we create
a large number of diversified metrics by randomizing a scaled exponential
similarity kernel, which are then coupled with random subspaces to form a large
set of metric-subspace pairs. Based on the similarity matrices derived from
these metric-subspace pairs, an ensemble of diversified base clusterings can
thereby be constructed. Thereafter, an entropy-based criterion is adopted to
explore the cluster-wise diversity in ensembles. By jointly exploiting the
multi-level diversity in metrics, subspaces, and clusters, three specific
ensemble clustering algorithms are finally presented. Experimental results on
30 real-world high-dimensional datasets (including 18 cancer gene expression
datasets and 12 image/speech datasets) have demonstrated the superiority of the
proposed algorithms over the state-of-the-art
An Automatic System for Unconstrained Video-Based Face Recognition
Although deep learning approaches have achieved performance surpassing humans
for still image-based face recognition, unconstrained video-based face
recognition is still a challenging task due to large volume of data to be
processed and intra/inter-video variations on pose, illumination, occlusion,
scene, blur, video quality, etc. In this work, we consider challenging
scenarios for unconstrained video-based face recognition from multiple-shot
videos and surveillance videos with low-quality frames. To handle these
problems, we propose a robust and efficient system for unconstrained
video-based face recognition, which is composed of modules for face/fiducial
detection, face association, and face recognition. First, we use multi-scale
single-shot face detectors to efficiently localize faces in videos. The
detected faces are then grouped respectively through carefully designed face
association methods, especially for multi-shot videos. Finally, the faces are
recognized by the proposed face matcher based on an unsupervised subspace
learning approach and a subspace-to-subspace similarity metric. Extensive
experiments on challenging video datasets, such as Multiple Biometric Grand
Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus
Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and
IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the
proposed system can accurately detect and associate faces from unconstrained
videos and effectively learn robust and discriminative features for
recognition
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested
information and knowledge from remote sensing (RS) images with high efficiency
and accuracy. However, with the development of RS technologies, RS system
provide images with higher spatial and temporal resolution and more spectral
channels than before, and it is inefficient and almost impossible to manually
interpret these images. Thus, it is of great interests to explore automatic and
intelligent algorithms to quickly process such massive RS data with high
accuracy. This thesis targets to develop some efficient information extraction
algorithms for RS images, by relying on the advanced technologies in machine
learning. More precisely, we adopt the manifold learning algorithms as the
mainline and unify the regularization theory, tensor-based method, sparse
learning and transfer learning into the same framework. The main contributions
of this thesis are as follows.Comment: 7 page
A Comprehensive Survey on Cross-modal Retrieval
In recent years, cross-modal retrieval has drawn much attention due to the
rapid growth of multimodal data. It takes one type of data as the query to
retrieve relevant data of another type. For example, a user can use a text to
retrieve relevant pictures or videos. Since the query and its retrieved results
can be of different modalities, how to measure the content similarity between
different modalities of data remains a challenge. Various methods have been
proposed to deal with such a problem. In this paper, we first review a number
of representative methods for cross-modal retrieval and classify them into two
main groups: 1) real-valued representation learning, and 2) binary
representation learning. Real-valued representation learning methods aim to
learn real-valued common representations for different modalities of data. To
speed up the cross-modal retrieval, a number of binary representation learning
methods are proposed to map different modalities of data into a common Hamming
space. Then, we introduce several multimodal datasets in the community, and
show the experimental results on two commonly used multimodal datasets. The
comparison reveals the characteristic of different kinds of cross-modal
retrieval methods, which is expected to benefit both practical applications and
future research. Finally, we discuss open problems and future research
directions.Comment: 20 pages, 11 figures, 9 table
A Unified Semi-Supervised Dimensionality Reduction Framework for Manifold Learning
We present a general framework of semi-supervised dimensionality reduction
for manifold learning which naturally generalizes existing supervised and
unsupervised learning frameworks which apply the spectral decomposition.
Algorithms derived under our framework are able to employ both labeled and
unlabeled examples and are able to handle complex problems where data form
separate clusters of manifolds. Our framework offers simple views, explains
relationships among existing frameworks and provides further extensions which
can improve existing algorithms. Furthermore, a new semi-supervised
kernelization framework called ``KPCA trick'' is proposed to handle non-linear
problems.Comment: 22 pages, 9 figure
Learning to Hash for Indexing Big Data - A Survey
The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution using exhaustive comparison is infeasible due to the prohibitive
computational complexity and memory requirement. In response, Approximate
Nearest Neighbor (ANN) search based on hashing techniques has become popular
due to its promising performance in both efficiency and accuracy. Prior
randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore
data-independent hash functions with random projections or permutations.
Although having elegant theoretic guarantees on the search quality in certain
metric spaces, performance of randomized hashing has been shown insufficient in
many real-world applications. As a remedy, new approaches incorporating
data-driven learning methods in development of advanced hash functions have
emerged. Such learning to hash methods exploit information such as data
distributions or class labels when optimizing the hash codes or functions.
Importantly, the learned hash codes are able to preserve the proximity of
neighboring data in the original feature spaces in the hash code spaces. The
goal of this paper is to provide readers with systematic understanding of
insights, pros and cons of the emerging techniques. We provide a comprehensive
survey of the learning to hash framework and representative techniques of
various types, including unsupervised, semi-supervised, and supervised. In
addition, we also summarize recent hashing approaches utilizing the deep
learning models. Finally, we discuss the future direction and trends of
research in this area
A Kernel Classification Framework for Metric Learning
Learning a distance metric from the given training samples plays a crucial
role in many machine learning tasks, and various models and optimization
algorithms have been proposed in the past decade. In this paper, we generalize
several state-of-the-art metric learning methods, such as large margin nearest
neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel
classification framework. First, doublets and triplets are constructed from the
training samples, and a family of degree-2 polynomial kernel functions are
proposed for pairs of doublets or triplets. Then, a kernel classification
framework is established, which can not only generalize many popular metric
learning methods such as LMNN and ITML, but also suggest new metric learning
methods, which can be efficiently implemented, interestingly, by using the
standard support vector machine (SVM) solvers. Two novel metric learning
methods, namely doublet-SVM and triplet-SVM, are then developed under the
proposed framework. Experimental results show that doublet-SVM and triplet-SVM
achieve competitive classification accuracies with state-of-the-art metric
learning methods such as ITML and LMNN but with significantly less training
time.Comment: 11 pages, 7 figure
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