15,285 research outputs found
Classification using log Gaussian Cox processes
McCullagh and Yang (2006) suggest a family of classification algorithms based
on Cox processes. We further investigate the log Gaussian variant which has a
number of appealing properties. Conditioned on the covariates, the distribution
over labels is given by a type of conditional Markov random field. In the
supervised case, computation of the predictive probability of a single test
point scales linearly with the number of training points and the multiclass
generalization is straightforward. We show new links between the supervised
method and classical nonparametric methods. We give a detailed analysis of the
pairwise graph representable Markov random field, which we use to extend the
model to semi-supervised learning problems, and propose an inference method
based on graph min-cuts. We give the first experimental analysis on supervised
and semi-supervised datasets and show good empirical performance.Comment: 17 pages, 6 figure
An unsupervised long short-term memory neural network for event detection in cell videos
We propose an automatic unsupervised cell event detection and classification
method, which expands convolutional Long Short-Term Memory (LSTM) neural
networks, for cellular events in cell video sequences. Cells in images that are
captured from various biomedical applications usually have different shapes and
motility, which pose difficulties for the automated event detection in cell
videos. Current methods to detect cellular events are based on supervised
machine learning and rely on tedious manual annotation from investigators with
specific expertise. So that our LSTM network could be trained in an
unsupervised manner, we designed it with a branched structure where one branch
learns the frequent, regular appearance and movements of objects and the second
learns the stochastic events, which occur rarely and without warning in a cell
video sequence. We tested our network on a publicly available dataset of
densely packed stem cell phase-contrast microscopy images undergoing cell
division. This dataset is considered to be more challenging that a dataset with
sparse cells. We compared our method to several published supervised methods
evaluated on the same dataset and to a supervised LSTM method with a similar
design and configuration to our unsupervised method. We used an F1-score, which
is a balanced measure for both precision and recall. Our results show that our
unsupervised method has a higher or similar F1-score when compared to two fully
supervised methods that are based on Hidden Conditional Random Fields (HCRF),
and has comparable accuracy with the current best supervised HCRF-based method.
Our method was generalizable as after being trained on one video it could be
applied to videos where the cells were in different conditions. The accuracy of
our unsupervised method approached that of its supervised counterpart
Semi-supervised learning for structured regression on partially observed attributed graphs
Conditional probabilistic graphical models provide a powerful framework for
structured regression in spatio-temporal datasets with complex correlation
patterns. However, in real-life applications a large fraction of observations
is often missing, which can severely limit the representational power of these
models. In this paper we propose a Marginalized Gaussian Conditional Random
Fields (m-GCRF) structured regression model for dealing with missing labels in
partially observed temporal attributed graphs. This method is aimed at learning
with both labeled and unlabeled parts and effectively predicting future values
in a graph. The method is even capable of learning from nodes for which the
response variable is never observed in history, which poses problems for many
state-of-the-art models that can handle missing data. The proposed model is
characterized for various missingness mechanisms on 500 synthetic graphs. The
benefits of the new method are also demonstrated on a challenging application
for predicting precipitation based on partial observations of climate variables
in a temporal graph that spans the entire continental US. We also show that the
method can be useful for optimizing the costs of data collection in climate
applications via active reduction of the number of weather stations to
consider. In experiments on these real-world and synthetic datasets we show
that the proposed model is consistently more accurate than alternative
semi-supervised structured models, as well as models that either use imputation
to deal with missing values or simply ignore them altogether.Comment: Proceedings of the 2015 SIAM International Conference on Data Mining
(SDM 2015) Vancouver, Canada, April 30 - May 02, 201
Machine learning based hyperspectral image analysis: A survey
Hyperspectral sensors enable the study of the chemical properties of scene
materials remotely for the purpose of identification, detection, and chemical
composition analysis of objects in the environment. Hence, hyperspectral images
captured from earth observing satellites and aircraft have been increasingly
important in agriculture, environmental monitoring, urban planning, mining, and
defense. Machine learning algorithms due to their outstanding predictive power
have become a key tool for modern hyperspectral image analysis. Therefore, a
solid understanding of machine learning techniques have become essential for
remote sensing researchers and practitioners. This paper reviews and compares
recent machine learning-based hyperspectral image analysis methods published in
literature. We organize the methods by the image analysis task and by the type
of machine learning algorithm, and present a two-way mapping between the image
analysis tasks and the types of machine learning algorithms that can be applied
to them. The paper is comprehensive in coverage of both hyperspectral image
analysis tasks and machine learning algorithms. The image analysis tasks
considered are land cover classification, target detection, unmixing, and
physical parameter estimation. The machine learning algorithms covered are
Gaussian models, linear regression, logistic regression, support vector
machines, Gaussian mixture model, latent linear models, sparse linear models,
Gaussian mixture models, ensemble learning, directed graphical models,
undirected graphical models, clustering, Gaussian processes, Dirichlet
processes, and deep learning. We also discuss the open challenges in the field
of hyperspectral image analysis and explore possible future directions
Face Clustering: Representation and Pairwise Constraints
Clustering face images according to their identity has two important
applications: (i) grouping a collection of face images when no external labels
are associated with images, and (ii) indexing for efficient large scale face
retrieval. The clustering problem is composed of two key parts: face
representation and choice of similarity for grouping faces. We first propose a
representation based on ResNet, which has been shown to perform very well in
image classification problems. Given this representation, we design a
clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly
estimates the adjacency matrix only based on the similarity between face
images. This allows a dynamic selection of number of clusters and retains
pairwise similarity between faces. ConPaC formulates the clustering problem as
a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to
find an approximate solution for maximizing the posterior probability of the
adjacency matrix. Experimental results on two benchmark face datasets (LFW and
IJB-B) show that ConPaC outperforms well known clustering algorithms such as
k-means, spectral clustering and approximate rank-order. Additionally, our
algorithm can naturally incorporate pairwise constraints to obtain a
semi-supervised version that leads to improved clustering performance. We also
propose an k-NN variant of ConPaC, which has a linear time complexity given a
k-NN graph, suitable for large datasets.Comment: This second version is the same as TIFS version. Some experiment
results are different from v1 because we correct the protocol
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
The amount of text that is generated every day is increasing dramatically.
This tremendous volume of mostly unstructured text cannot be simply processed
and perceived by computers. Therefore, efficient and effective techniques and
algorithms are required to discover useful patterns. Text mining is the task of
extracting meaningful information from text, which has gained significant
attentions in recent years. In this paper, we describe several of the most
fundamental text mining tasks and techniques including text pre-processing,
classification and clustering. Additionally, we briefly explain text mining in
biomedical and health care domains.Comment: some of References format have update
Text Classification Algorithms: A Survey
In recent years, there has been an exponential growth in the number of
complex documents and texts that require a deeper understanding of machine
learning methods to be able to accurately classify texts in many applications.
Many machine learning approaches have achieved surpassing results in natural
language processing. The success of these learning algorithms relies on their
capacity to understand complex models and non-linear relationships within data.
However, finding suitable structures, architectures, and techniques for text
classification is a challenge for researchers. In this paper, a brief overview
of text classification algorithms is discussed. This overview covers different
text feature extractions, dimensionality reduction methods, existing algorithms
and techniques, and evaluations methods. Finally, the limitations of each
technique and their application in the real-world problem are discussed
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
Time-domain astronomy (TDA) is facing a paradigm shift caused by the
exponential growth of the sample size, data complexity and data generation
rates of new astronomical sky surveys. For example, the Large Synoptic Survey
Telescope (LSST), which will begin operations in northern Chile in 2022, will
generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky.
The LSST will stream data at rates of 2 Terabytes per hour, effectively
capturing an unprecedented movie of the sky. The LSST is expected not only to
improve our understanding of time-varying astrophysical objects, but also to
reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with
a change of paradigm to data-driven astronomy, the fields of astroinformatics
and astrostatistics have been created recently. The new data-oriented paradigms
for astronomy combine statistics, data mining, knowledge discovery, machine
learning and computational intelligence, in order to provide the automated and
robust methods needed for the rapid detection and classification of known
astrophysical objects as well as the unsupervised characterization of novel
phenomena. In this article we present an overview of machine learning and
computational intelligence applications to TDA. Future big data challenges and
new lines of research in TDA, focusing on the LSST, are identified and
discussed from the viewpoint of computational intelligence/machine learning.
Interdisciplinary collaboration will be required to cope with the challenges
posed by the deluge of astronomical data coming from the LSST
A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling
Human behavior is a continuous stochastic spatio-temporal process which is
governed by semantic actions and affordances as well as latent factors.
Therefore, video-based human activity modeling is concerned with a number of
tasks such as inferring current and future semantic labels, predicting future
continuous observations as well as imagining possible future label and feature
sequences. In this paper we present a semi-supervised probabilistic deep latent
variable model that can represent both discrete labels and continuous
observations as well as latent dynamics over time. This allows the model to
solve several tasks at once without explicit fine-tuning. We focus here on the
tasks of action classification, detection, prediction and anticipation as well
as motion prediction and synthesis based on 3D human activity data recorded
with Kinect. We further extend the model to capture hierarchical label
structure and to model the dependencies between multiple entities, such as a
human and objects. Our experiments demonstrate that our principled approach to
human activity modeling can be used to detect current and anticipate future
semantic labels and to predict and synthesize future label and feature
sequences. When comparing our model to state-of-the-art approaches, which are
specifically designed for e.g. action classification, we find that our
probabilistic formulation outperforms or is comparable to these task specific
models
Natural Language Processing (almost) from Scratch
We propose a unified neural network architecture and learning algorithm that
can be applied to various natural language processing tasks including:
part-of-speech tagging, chunking, named entity recognition, and semantic role
labeling. This versatility is achieved by trying to avoid task-specific
engineering and therefore disregarding a lot of prior knowledge. Instead of
exploiting man-made input features carefully optimized for each task, our
system learns internal representations on the basis of vast amounts of mostly
unlabeled training data. This work is then used as a basis for building a
freely available tagging system with good performance and minimal computational
requirements
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