1,302 research outputs found
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings
The recovery of the intrinsic geometric structures of data collections is an
important problem in data analysis. Supervised extensions of several manifold
learning approaches have been proposed in the recent years. Meanwhile, existing
methods primarily focus on the embedding of the training data, and the
generalization of the embedding to initially unseen test data is rather
ignored. In this work, we build on recent theoretical results on the
generalization performance of supervised manifold learning algorithms.
Motivated by these performance bounds, we propose a supervised manifold
learning method that computes a nonlinear embedding while constructing a smooth
and regular interpolation function that extends the embedding to the whole data
space in order to achieve satisfactory generalization. The embedding and the
interpolator are jointly learnt such that the Lipschitz regularity of the
interpolator is imposed while ensuring the separation between different
classes. Experimental results on several image data sets show that the proposed
method outperforms traditional classifiers and the supervised dimensionality
reduction algorithms in comparison in terms of classification accuracy in most
settings
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202
Kernel-Based Ranking. Methods for Learning and Performance Estimation
Machine learning provides tools for automated construction of predictive
models in data intensive areas of engineering and science. The family of
regularized kernel methods have in the recent years become one of the mainstream
approaches to machine learning, due to a number of advantages the
methods share. The approach provides theoretically well-founded solutions
to the problems of under- and overfitting, allows learning from structured
data, and has been empirically demonstrated to yield high predictive performance
on a wide range of application domains. Historically, the problems
of classification and regression have gained the majority of attention in the
field. In this thesis we focus on another type of learning problem, that of
learning to rank.
In learning to rank, the aim is from a set of past observations to learn
a ranking function that can order new objects according to how well they
match some underlying criterion of goodness. As an important special case
of the setting, we can recover the bipartite ranking problem, corresponding
to maximizing the area under the ROC curve (AUC) in binary classification.
Ranking applications appear in a large variety of settings, examples
encountered in this thesis include document retrieval in web search, recommender
systems, information extraction and automated parsing of natural
language. We consider the pairwise approach to learning to rank, where
ranking models are learned by minimizing the expected probability of ranking
any two randomly drawn test examples incorrectly. The development
of computationally efficient kernel methods, based on this approach, has in
the past proven to be challenging. Moreover, it is not clear what techniques
for estimating the predictive performance of learned models are the most
reliable in the ranking setting, and how the techniques can be implemented
efficiently.
The contributions of this thesis are as follows. First, we develop
RankRLS, a computationally efficient kernel method for learning to rank,
that is based on minimizing a regularized pairwise least-squares loss. In
addition to training methods, we introduce a variety of algorithms for tasks
such as model selection, multi-output learning, and cross-validation, based
on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm,
which is one of the most well established methods for learning to
rank. Third, we study the combination of the empirical kernel map and reduced
set approximation, which allows the large-scale training of kernel machines
using linear solvers, and propose computationally efficient solutions
to cross-validation when using the approach. Next, we explore the problem
of reliable cross-validation when using AUC as a performance criterion,
through an extensive simulation study. We demonstrate that the proposed
leave-pair-out cross-validation approach leads to more reliable performance
estimation than commonly used alternative approaches. Finally, we present
a case study on applying machine learning to information extraction from
biomedical literature, which combines several of the approaches considered
in the thesis. The thesis is divided into two parts. Part I provides the background
for the research work and summarizes the most central results, Part
II consists of the five original research articles that are the main contribution
of this thesis.Siirretty Doriast
Graph enabled cross-domain knowledge transfer
The world has never been more connected, led by the information technology revolution in the past decades that has fundamentally changed the way people interact with each other using social networks. Consequently, enormous human activity data are collected from the business world and machine learning techniques are widely adopted to aid our decision processes. Despite of the success of machine learning in various application scenarios, there are still many questions that need to be well answered, such as optimizing machine learning outcomes when desired knowledge cannot be extracted from the available data. This naturally drives us to ponder if one can leverage some side information to populate the knowledge domain of their interest, such that the problems within that knowledge domain can be better tackled.
In this work, such problems are investigated and practical solutions are proposed. To leverage machine learning in any decision-making process, one must convert the given knowledge (for example, natural language, unstructured text) into representation vectors that can be understood and processed by machine learning model in their compatible language and data format. The frequently encountered difficulty is, however, the given knowledge is not rich or reliable enough in the first place. In such cases, one seeks to fuse side information from a separate domain to mitigate the gap between good representation learning and the scarce knowledge in the domain of interest. This approach is named Cross-Domain Knowledge Transfer. It is crucial to study the problem because of the commonality of scarce knowledge in many scenarios, from online healthcare platform analyses to financial market risk quantification, leaving an obstacle in front of us benefiting from automated decision making. From the machine learning perspective, the paradigm of semi-supervised learning takes advantage of large amount of data without ground truth and achieves impressive learning performance improvement. It is adopted in this dissertation for cross-domain knowledge transfer.
Furthermore, graph learning techniques are indispensable given that networks commonly exist in real word, such as taxonomy networks and scholarly article citation networks. These networks contain additional useful knowledge and are ought to be incorporated in the learning process, which serve as an important lever in solving the problem of cross-domain knowledge transfer. This dissertation proposes graph-based learning solutions and demonstrates their practical usage via empirical studies on real-world applications. Another line of effort in this work lies in leveraging the rich capacity of neural networks to improve the learning outcomes, as we are in the era of big data.
In contrast to many Graph Neural Networks that directly iterate on the graph adjacency to approximate graph convolution filters, this work also proposes an efficient Eigenvalue learning method that directly optimizes the graph convolution in the spectral space. This work articulates the importance of network spectrum and provides detailed analyses on the spectral properties in the proposed EigenLearn method, which well aligns with a series of CNN models that attempt to have meaningful spectral interpretation in designing graph neural networks. The disser-tation also addresses the efficiency, which can be categorized in two folds. First, by adopting approximate solutions it mitigates the complexity concerns for graph related algorithms, which are naturally quadratic in most cases and do not scale to large datasets. Second, it mitigates the storage and computation overhead in deep neural network, such that they can be deployed on many light-weight devices and significantly broaden the applicability. Finally, the dissertation is concluded by future endeavors
Graph Enabled Cross-Domain Knowledge Transfer
To leverage machine learning in any decision-making process, one must convert
the given knowledge (for example, natural language, unstructured text) into
representation vectors that can be understood and processed by machine learning
model in their compatible language and data format. The frequently encountered
difficulty is, however, the given knowledge is not rich or reliable enough in
the first place. In such cases, one seeks to fuse side information from a
separate domain to mitigate the gap between good representation learning and
the scarce knowledge in the domain of interest. This approach is named
Cross-Domain Knowledge Transfer. It is crucial to study the problem because of
the commonality of scarce knowledge in many scenarios, from online healthcare
platform analyses to financial market risk quantification, leaving an obstacle
in front of us benefiting from automated decision making. From the machine
learning perspective, the paradigm of semi-supervised learning takes advantage
of large amount of data without ground truth and achieves impressive learning
performance improvement. It is adopted in this dissertation for cross-domain
knowledge transfer. (to be continued
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