1,556 research outputs found
Realtime market microstructure analysis: online Transaction Cost Analysis
Motivated by the practical challenge in monitoring the performance of a large
number of algorithmic trading orders, this paper provides a methodology that
leads to automatic discovery of the causes that lie behind a poor trading
performance. It also gives theoretical foundations to a generic framework for
real-time trading analysis. Academic literature provides different ways to
formalize these algorithms and show how optimal they can be from a
mean-variance, a stochastic control, an impulse control or a statistical
learning viewpoint. This paper is agnostic about the way the algorithm has been
built and provides a theoretical formalism to identify in real-time the market
conditions that influenced its efficiency or inefficiency. For a given set of
characteristics describing the market context, selected by a practitioner, we
first show how a set of additional derived explanatory factors, called anomaly
detectors, can be created for each market order. We then will present an online
methodology to quantify how this extended set of factors, at any given time,
predicts which of the orders are underperforming while calculating the
predictive power of this explanatory factor set. Armed with this information,
which we call influence analysis, we intend to empower the order monitoring
user to take appropriate action on any affected orders by re-calibrating the
trading algorithms working the order through new parameters, pausing their
execution or taking over more direct trading control. Also we intend that use
of this method in the post trade analysis of algorithms can be taken advantage
of to automatically adjust their trading action.Comment: 33 pages, 12 figure
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Fusing data from multiple modalities provides more information to train
machine learning systems. However, it is prohibitively expensive and
time-consuming to label each modality with a large amount of data, which leads
to a crucial problem of semi-supervised multi-modal learning. Existing methods
suffer from either ineffective fusion across modalities or lack of theoretical
guarantees under proper assumptions. In this paper, we propose a novel
information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation
\textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal
learning, which is endowed with promising properties: (i) it can utilize
effectively the information across different modalities of unlabeled data
points to facilitate training classifiers of each modality (ii) it has
theoretical guarantee to identify Bayesian classifiers, i.e., the ground truth
posteriors of all modalities. Specifically, by maximizing TC-induced loss
(namely TC gain) over classifiers of all modalities, these classifiers can
cooperatively discover the equivalent class of ground-truth classifiers; and
identify the unique ones by leveraging limited percentage of labeled data. We
apply our method to various tasks and achieve state-of-the-art results,
including news classification, emotion recognition and disease prediction.Comment: ECCV 2020 (oral
Intrasubject multimodal groupwise registration with the conditional template entropy
Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information
3D nonrigid medical image registration using a new information theoretic measure.
International audienceThis work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy
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