16,879 research outputs found
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to
Signal Processing: Image Communication journa
Decomposition tables for experiments. II. Two--one randomizations
We investigate structure for pairs of randomizations that do not follow each
other in a chain. These are unrandomized-inclusive, independent, coincident or
double randomizations. This involves taking several structures that satisfy
particular relations and combining them to form the appropriate orthogonal
decomposition of the data space for the experiment. We show how to establish
the decomposition table giving the sources of variation, their relationships
and their degrees of freedom, so that competing designs can be evaluated. This
leads to recommendations for when the different types of multiple randomization
should be used.Comment: Published in at http://dx.doi.org/10.1214/09-AOS785 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Learning continuous representations of nodes is attracting growing interest
in both academia and industry recently, due to their simplicity and
effectiveness in a variety of applications. Most of existing node embedding
algorithms and systems are capable of processing networks with hundreds of
thousands or a few millions of nodes. However, how to scale them to networks
that have tens of millions or even hundreds of millions of nodes remains a
challenging problem. In this paper, we propose GraphVite, a high-performance
CPU-GPU hybrid system for training node embeddings, by co-optimizing the
algorithm and the system. On the CPU end, augmented edge samples are parallelly
generated by random walks in an online fashion on the network, and serve as the
training data. On the GPU end, a novel parallel negative sampling is proposed
to leverage multiple GPUs to train node embeddings simultaneously, without much
data transfer and synchronization. Moreover, an efficient collaboration
strategy is proposed to further reduce the synchronization cost between CPUs
and GPUs. Experiments on multiple real-world networks show that GraphVite is
super efficient. It takes only about one minute for a network with 1 million
nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20
hours for a network with 66 million nodes and 1.8 billion edges. Compared to
the current fastest system, GraphVite is about 50 times faster without any
sacrifice on performance.Comment: accepted at WWW 201
Relativistic Quantum Mechanics - Particle Production and Cluster Properties
This paper constructs relativistic quantum mechanical models of particles
satisfying cluster properties and the spectral condition which do not conserve
particle number. The treatment of particle production is limited to systems
with a bounded number of bare-particle degrees of freedom. The focus of this
paper is about the realization of cluster properties in these theories.Comment: 36 pages, Late
PLS dimension reduction for classification of microarray data
PLS dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, PLS is compared with some of the best state-of-the-art classification methods. In addition, a simple procedure to choose the number of components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proven. PLS can also be used as a visualization tool for high-dimensional data in the classification framework. The whole study is based on 9 real microarray cancer data sets
Solving for multi-class using orthogonal coding matrices
A common method of generalizing binary to multi-class classification is the
error correcting code (ECC). ECCs may be optimized in a number of ways, for
instance by making them orthogonal. Here we test two types of orthogonal ECCs
on seven different datasets using three types of binary classifier and compare
them with three other multi-class methods: 1 vs. 1, one-versus-the-rest and
random ECCs. The first type of orthogonal ECC, in which the codes contain no
zeros, admits a fast and simple method of solving for the probabilities.
Orthogonal ECCs are always more accurate than random ECCs as predicted by
recent literature. Improvments in uncertainty coefficient (U.C.) range between
0.4--17.5% (0.004--0.139, absolute), while improvements in Brier score between
0.7--10.7%. Unfortunately, orthogonal ECCs are rarely more accurate than 1 vs.
1. Disparities are worst when the methods are paired with logistic regression,
with orthogonal ECCs never beating 1 vs. 1. When the methods are paired with
SVM, the losses are less significant, peaking at 1.5%, relative, 0.011 absolute
in uncertainty coefficient and 6.5% in Brier scores. Orthogonal ECCs are always
the fastest of the five multi-class methods when paired with linear
classifiers. When paired with a piecewise linear classifier, whose
classification speed does not depend on the number of training samples,
classifications using orthogonal ECCs were always more accurate than the the
remaining three methods and also faster than 1 vs. 1. Losses against 1 vs. 1
here were higher, peaking at 1.9% (0.017, absolute), in U.C. and 39% in Brier
score. Gains in speed ranged between 1.1% and over 100%. Whether the speed
increase is worth the penalty in accuracy will depend on the application
A sparse decomposition of low rank symmetric positive semi-definite matrices
Suppose that is symmetric positive
semidefinite with rank . Our goal is to decompose into
rank-one matrices where the modes
are required to be as sparse as possible. In contrast to eigen decomposition,
these sparse modes are not required to be orthogonal. Such a problem arises in
random field parametrization where is the covariance function and is
intractable to solve in general. In this paper, we partition the indices from 1
to into several patches and propose to quantify the sparseness of a vector
by the number of patches on which it is nonzero, which is called patch-wise
sparseness. Our aim is to find the decomposition which minimizes the total
patch-wise sparseness of the decomposed modes. We propose a
domain-decomposition type method, called intrinsic sparse mode decomposition
(ISMD), which follows the "local-modes-construction + patching-up" procedure.
The key step in the ISMD is to construct local pieces of the intrinsic sparse
modes by a joint diagonalization problem. Thereafter a pivoted Cholesky
decomposition is utilized to glue these local pieces together. Optimal sparse
decomposition, consistency with different domain decomposition and robustness
to small perturbation are proved under the so called regular-sparse assumption
(see Definition 1.2). We provide simulation results to show the efficiency and
robustness of the ISMD. We also compare the ISMD to other existing methods,
e.g., eigen decomposition, pivoted Cholesky decomposition and convex relaxation
of sparse principal component analysis [25] and [40]
- …