10,410 research outputs found
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment
Multi-view high-dimensional data become increasingly popular in the big data
era. Feature selection is a useful technique for alleviating the curse of
dimensionality in multi-view learning. In this paper, we study unsupervised
feature selection for multi-view data, as class labels are usually expensive to
obtain. Traditional feature selection methods are mostly designed for
single-view data and cannot fully exploit the rich information from multi-view
data. Existing multi-view feature selection methods are usually based on noisy
cluster labels which might not preserve sufficient information from multi-view
data. To better utilize multi-view information, we propose a method, CDMA-FS,
to select features for each view by performing alignment on a cross diffused
matrix. We formulate it as a constrained optimization problem and solve it
using Quasi-Newton based method. Experiments results on four real-world
datasets show that the proposed method is more effective than the
state-of-the-art methods in multi-view setting.Comment: 8 page
Unsupervised Meta-path Reduction on Heterogeneous Information Networks
Heterogeneous Information Network (HIN) has attracted much attention due to
its wide applicability in a variety of data mining tasks, especially for tasks
with multi-typed objects. A potentially large number of meta-paths can be
extracted from the heterogeneous networks, providing abundant semantic
knowledge. Though a variety of meta-paths can be defined, too many meta-paths
are redundant. Reduction on the number of meta-paths can enhance the
effectiveness since some redundant meta-paths provide interferential linkage to
the task. Moreover, the reduced meta-paths can reflect the characteristic of
the heterogeneous network. Previous endeavors try to reduce the number of
meta-paths under the guidance of supervision information. Nevertheless,
supervised information is expensive and may not always be available. In this
paper, we propose a novel algorithm, SPMR (Semantic Preserving Meta-path
Reduction), to reduce a set of pre-defined meta-paths in an unsupervised
setting. The proposed method is able to evaluate a set of meta-paths to
maximally preserve the semantics of original meta-paths after reduction.
Experimental results show that SPMR can select a succinct subset of meta-paths
which can achieve comparable or even better performance with fewer meta-paths
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its
aim is to leverage useful information contained in multiple related tasks to
help improve the generalization performance of all the tasks. In this paper, we
give a survey for MTL. First, we classify different MTL algorithms into several
categories, including feature learning approach, low-rank approach, task
clustering approach, task relation learning approach, and decomposition
approach, and then discuss the characteristics of each approach. In order to
improve the performance of learning tasks further, MTL can be combined with
other learning paradigms including semi-supervised learning, active learning,
unsupervised learning, reinforcement learning, multi-view learning and
graphical models. When the number of tasks is large or the data dimensionality
is high, batch MTL models are difficult to handle this situation and online,
parallel and distributed MTL models as well as dimensionality reduction and
feature hashing are reviewed to reveal their computational and storage
advantages. Many real-world applications use MTL to boost their performance and
we review representative works. Finally, we present theoretical analyses and
discuss several future directions for MTL
Feature Selection: A Data Perspective
Feature selection, as a data preprocessing strategy, has been proven to be
effective and efficient in preparing data (especially high-dimensional data)
for various data mining and machine learning problems. The objectives of
feature selection include: building simpler and more comprehensible models,
improving data mining performance, and preparing clean, understandable data.
The recent proliferation of big data has presented some substantial challenges
and opportunities to feature selection. In this survey, we provide a
comprehensive and structured overview of recent advances in feature selection
research. Motivated by current challenges and opportunities in the era of big
data, we revisit feature selection research from a data perspective and review
representative feature selection algorithms for conventional data, structured
data, heterogeneous data and streaming data. Methodologically, to emphasize the
differences and similarities of most existing feature selection algorithms for
conventional data, we categorize them into four main groups: similarity based,
information theoretical based, sparse learning based and statistical based
methods. To facilitate and promote the research in this community, we also
present an open-source feature selection repository that consists of most of
the popular feature selection algorithms
(\url{http://featureselection.asu.edu/}). Also, we use it as an example to show
how to evaluate feature selection algorithms. At the end of the survey, we
present a discussion about some open problems and challenges that require more
attention in future research
Unsupervised Feature Selection via Multi-step Markov Transition Probability
Feature selection is a widely used dimension reduction technique to select
feature subsets because of its interpretability. Many methods have been
proposed and achieved good results, in which the relationships between adjacent
data points are mainly concerned. But the possible associations between data
pairs that are may not adjacent are always neglected. Different from previous
methods, we propose a novel and very simple approach for unsupervised feature
selection, named MMFS (Multi-step Markov transition probability for Feature
Selection). The idea is using multi-step Markov transition probability to
describe the relation between any data pair. Two ways from the positive and
negative viewpoints are employed respectively to keep the data structure after
feature selection. From the positive viewpoint, the maximum transition
probability that can be reached in a certain number of steps is used to
describe the relation between two points. Then, the features which can keep the
compact data structure are selected. From the viewpoint of negative, the
minimum transition probability that can be reached in a certain number of steps
is used to describe the relation between two points. On the contrary, the
features that least maintain the loose data structure are selected. And the two
ways can also be combined. Thus three algorithms are proposed. Our main
contributions are a novel feature section approach which uses multi-step
transition probability to characterize the data structure, and three algorithms
proposed from the positive and negative aspects for keeping data structure. The
performance of our approach is compared with the state-of-the-art methods on
eight real-world data sets, and the experimental results show that the proposed
MMFS is effective in unsupervised feature selection
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
Clustering with Similarity Preserving
Graph-based clustering has shown promising performance in many tasks. A key
step of graph-based approach is the similarity graph construction. In general,
learning graph in kernel space can enhance clustering accuracy due to the
incorporation of nonlinearity. However, most existing kernel-based graph
learning mechanisms is not similarity-preserving, hence leads to sub-optimal
performance. To overcome this drawback, we propose a more discriminative graph
learning method which can preserve the pairwise similarities between samples in
an adaptive manner for the first time. Specifically, we require the learned
graph be close to a kernel matrix, which serves as a measure of similarity in
raw data. Moreover, the structure is adaptively tuned so that the number of
connected components of the graph is exactly equal to the number of clusters.
Finally, our method unifies clustering and graph learning which can directly
obtain cluster indicators from the graph itself without performing further
clustering step. The effectiveness of this approach is examined on both single
and multiple kernel learning scenarios in several datasets
Learning for Multi-Model and Multi-Type Fitting
Multi-model fitting has been extensively studied from the random sampling and
clustering perspectives. Most assume that only a single type/class of model is
present and their generalizations to fitting multiple types of
models/structures simultaneously are non-trivial. The inherent challenges
include choice of types and numbers of models, sampling imbalance and parameter
tuning, all of which render conventional approaches ineffective. In this work,
we formulate the multi-model multi-type fitting problem as one of learning deep
feature embedding that is clustering-friendly. In other words, points of the
same clusters are embedded closer together through the network. For inference,
we apply K-means to cluster the data in the embedded feature space and model
selection is enabled by analyzing the K-means residuals. Experiments are
carried out on both synthetic and real world multi-type fitting datasets,
producing state-of-the-art results. Comparisons are also made on single-type
multi-model fitting tasks with promising results as well
Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
For many image clustering problems, replacing raw image data with features
extracted by a pretrained convolutional neural network (CNN), leads to better
clustering performance. However, the specific features extracted, and, by
extension, the selected CNN architecture, can have a major impact on the
clustering results. In practice, this crucial design choice is often decided
arbitrarily due to the impossibility of using cross-validation with
unsupervised learning problems. However, information contained in the different
pretrained CNN architectures may be complementary, even when pretrained on the
same data. To improve clustering performance, we rephrase the image clustering
problem as a multi-view clustering (MVC) problem that considers multiple
different pretrained feature extractors as different "views" of the same data.
We then propose a multi-input neural network architecture that is trained
end-to-end to solve the MVC problem effectively. Our experimental results,
conducted on three different natural image datasets, show that: 1. using
multiple pretrained CNNs jointly as feature extractors improves image
clustering; 2. using an end-to-end approach improves MVC; and 3. combining both
produces state-of-the-art results for the problem of image clustering.Comment: 13 pages, 3 figures, 4 tables. Poster presentation at BMVC 2018
(29.9% acceptance
Deep Spectral Clustering using Dual Autoencoder Network
The clustering methods have recently absorbed even-increasing attention in
learning and vision. Deep clustering combines embedding and clustering together
to obtain optimal embedding subspace for clustering, which can be more
effective compared with conventional clustering methods. In this paper, we
propose a joint learning framework for discriminative embedding and spectral
clustering. We first devise a dual autoencoder network, which enforces the
reconstruction constraint for the latent representations and their noisy
versions, to embed the inputs into a latent space for clustering. As such the
learned latent representations can be more robust to noise. Then the mutual
information estimation is utilized to provide more discriminative information
from the inputs. Furthermore, a deep spectral clustering method is applied to
embed the latent representations into the eigenspace and subsequently clusters
them, which can fully exploit the relationship between inputs to achieve
optimal clustering results. Experimental results on benchmark datasets show
that our method can significantly outperform state-of-the-art clustering
approaches
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