49,963 research outputs found
Strong Sure Screening of Ultra-high Dimensional Categorical Data
Feature screening for ultra high dimensional feature spaces plays a critical
role in the analysis of data sets whose predictors exponentially exceed the
number of observations. Such data sets are becoming increasingly prevalent in
areas such as bioinformatics, medical imaging, and social network analysis.
Frequently, these data sets have both categorical response and categorical
covariates, yet extant feature screening literature rarely considers such data
types. We propose a new screening procedure rooted in the Cochran-Armitage
trend test. Our method is specifically applicable for data where both the
response and predictors are categorical. Under a set of reasonable conditions,
we demonstrate that our screening procedure has the strong sure screening
property, which extends the seminal results of Fan and Lv. A series of four
simulations are used to investigate the performance of our method relative to
three other screening methods. We also apply a two-stage iterative approach to
a real data example by first employing our proposed method, and then further
screening a subset of selected covariates using lasso, adaptive-lasso and
elastic net regularization.Comment: Preprint of Draf
Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier
Multiview learning problem refers to the problem of learning a classifier
from multiple view data. In this data set, each data points is presented by
multiple different views. In this paper, we propose a novel method for this
problem. This method is based on two assumptions. The first assumption is that
each data point has an intact feature vector, and each view is obtained by a
linear transformation from the intact vector. The second assumption is that the
intact vectors are discriminative, and in the intact space, we have a linear
classifier to separate the positive class from the negative class. We define an
intact vector for each data point, and a view-conditional transformation matrix
for each view, and propose to reconstruct the multiple view feature vectors by
the product of the corresponding intact vectors and transformation matrices.
Moreover, we also propose a linear classifier in the intact space, and learn it
jointly with the intact vectors. The learning problem is modeled by a
minimization problem, and the objective function is composed of a Cauchy error
estimator-based view-conditional reconstruction term over all data points and
views, and a classification error term measured by hinge loss over all the
intact vectors of all the data points. Some regularization terms are also
imposed to different variables in the objective function. The minimization
problem is solve by an iterative algorithm using alternate optimization
strategy and gradient descent algorithm. The proposed algorithm shows it
advantage in the compression to other multiview learning algorithms on
benchmark data sets
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Multi-label learning draws great interests in many real world applications.
It is a highly costly task to assign many labels by the oracle for one
instance. Meanwhile, it is also hard to build a good model without diagnosing
discriminative labels. Can we reduce the label costs and improve the ability to
train a good model for multi-label learning simultaneously?
Active learning addresses the less training samples problem by querying the
most valuable samples to achieve a better performance with little costs. In
multi-label active learning, some researches have been done for querying the
relevant labels with less training samples or querying all labels without
diagnosing the discriminative information. They all cannot effectively handle
the outlier labels for the measurement of uncertainty. Since Maximum
Correntropy Criterion (MCC) provides a robust analysis for outliers in many
machine learning and data mining algorithms, in this paper, we derive a robust
multi-label active learning algorithm based on MCC by merging uncertainty and
representativeness, and propose an efficient alternating optimization method to
solve it. With MCC, our method can eliminate the influence of outlier labels
that are not discriminative to measure the uncertainty. To make further
improvement on the ability of information measurement, we merge uncertainty and
representativeness with the prediction labels of unknown data. It can not only
enhance the uncertainty but also improve the similarity measurement of
multi-label data with labels information. Experiments on benchmark multi-label
data sets have shown a superior performance than the state-of-the-art methods
Nonparametric Independence Screening via Favored Smoothing Bandwidth
We propose a flexible nonparametric regression method for
ultrahigh-dimensional data. As a first step, we propose a fast screening method
based on the favored smoothing bandwidth of the marginal local constant
regression. Then, an iterative procedure is developed to recover both the
important covariates and the regression function. Theoretically, we prove that
the favored smoothing bandwidth based screening possesses the model selection
consistency property. Simulation studies as well as real data analysis show the
competitive performance of the new procedure.Comment: 22 page
Deep Part Induction from Articulated Object Pairs
Object functionality is often expressed through part articulation -- as when
the two rigid parts of a scissor pivot against each other to perform the
cutting function. Such articulations are often similar across objects within
the same functional category. In this paper, we explore how the observation of
different articulation states provides evidence for part structure and motion
of 3D objects. Our method takes as input a pair of unsegmented shapes
representing two different articulation states of two functionally related
objects, and induces their common parts along with their underlying rigid
motion. This is a challenging setting, as we assume no prior shape structure,
no prior shape category information, no consistent shape orientation, the
articulation states may belong to objects of different geometry, plus we allow
inputs to be noisy and partial scans, or point clouds lifted from RGB images.
Our method learns a neural network architecture with three modules that
respectively propose correspondences, estimate 3D deformation flows, and
perform segmentation. To achieve optimal performance, our architecture
alternates between correspondence, deformation flow, and segmentation
prediction iteratively in an ICP-like fashion. Our results demonstrate that our
method significantly outperforms state-of-the-art techniques in the task of
discovering articulated parts of objects. In addition, our part induction is
object-class agnostic and successfully generalizes to new and unseen objects
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
Unsupervised Multi-modal Hashing for Cross-modal retrieval
With the advantage of low storage cost and high efficiency, hashing learning
has received much attention in the domain of Big Data. In this paper, we
propose a novel unsupervised hashing learning method to cope with this open
problem to directly preserve the manifold structure by hashing. To address this
problem, both the semantic correlation in textual space and the locally
geometric structure in the visual space are explored simultaneously in our
framework. Besides, the `2;1-norm constraint is imposed on the projection
matrices to learn the discriminative hash function for each modality. Extensive
experiments are performed to evaluate the proposed method on the three publicly
available datasets and the experimental results show that our method can
achieve superior performance over the state-of-the-art methods.Comment: 4 pages, 4 figure
Nasal Patches and Curves for Expression-robust 3D Face Recognition
The potential of the nasal region for expression robust 3D face recognition
is thoroughly investigated by a novel five-step algorithm. First, the nose tip
location is coarsely detected and the face is segmented, aligned and the nasal
region cropped. Then, a very accurate and consistent nasal landmarking
algorithm detects seven keypoints on the nasal region. In the third step, a
feature extraction algorithm based on the surface normals of Gabor-wavelet
filtered depth maps is utilised and, then, a set of spherical patches and
curves are localised over the nasal region to provide the feature descriptors.
The last step applies a genetic algorithm-based feature selector to detect the
most stable patches and curves over different facial expressions. The algorithm
provides the highest reported nasal region-based recognition ranks on the FRGC,
Bosphorus and BU-3DFE datasets. The results are comparable with, and in many
cases better than, many state-of-the-art 3D face recognition algorithms, which
use the whole facial domain. The proposed method does not rely on sophisticated
alignment or denoising steps, is very robust when only one sample per subject
is used in the gallery, and does not require a training step for the
landmarking algorithm. https://github.com/mehryaragha/NoseBiometric
Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
The sparse representation classifier (SRC) has been utilized in various
classification problems, which makes use of L1 minimization and works well for
image recognition satisfying a subspace assumption. In this paper we propose a
new implementation of SRC via screening, establish its equivalence to the
original SRC under regularity conditions, and prove its classification
consistency under a latent subspace model and contamination. The results are
demonstrated via simulations and real data experiments, where the new algorithm
achieves comparable numerical performance and significantly faster.Comment: 15 pages, 4 figures, 3 table
Relief-Based Feature Selection: Introduction and Review
Feature selection plays a critical role in biomedical data mining, driven by
increasing feature dimensionality in target problems and growing interest in
advanced but computationally expensive methodologies able to model complex
associations. Specifically, there is a need for feature selection methods that
are computationally efficient, yet sensitive to complex patterns of
association, e.g. interactions, so that informative features are not mistakenly
eliminated prior to downstream modeling. This paper focuses on Relief-based
algorithms (RBAs), a unique family of filter-style feature selection algorithms
that have gained appeal by striking an effective balance between these
objectives while flexibly adapting to various data characteristics, e.g.
classification vs. regression. First, this work broadly examines types of
feature selection and defines RBAs within that context. Next, we introduce the
original Relief algorithm and associated concepts, emphasizing the intuition
behind how it works, how feature weights generated by the algorithm can be
interpreted, and why it is sensitive to feature interactions without evaluating
combinations of features. Lastly, we include an expansive review of RBA
methodological research beyond Relief and its popular descendant, ReliefF. In
particular, we characterize branches of RBA research, and provide comparative
summaries of RBA algorithms including contributions, strategies, functionality,
time complexity, adaptation to key data characteristics, and software
availability.Comment: Submitted revisions for publication based on reviews by the Journal
of Biomedical Informatic
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