Rigorous and proper linking of financial databases is a necessary step to test trading strategies... more Rigorous and proper linking of financial databases is a necessary step to test trading strategies incorporating multimodal sources of information. This paper proposes a machine learning solution to match companies in heterogeneous financial databases. Our method, named Financial Attribute Selection Distance (FASD), has two stages, each of them corresponding to one of the two interrelated tasks commonly involved in heterogeneous database matching problems: schema matching and entity matching. FASD's schema matching procedure is based on the Kullback-Leibler divergence of string and numeric attributes. FASD's entity matching solution relies on learning a company distance flexible enough to deal with the numeric and string attribute links found by the schema matching algorithm, and it incorporates different string matching approaches such as edit-based and token-based metrics. The parameters of the distance are optimized using the F-score as cost function. FASD is able to match the joint Compustat/CRSP and Institutional Brokers' Estimate System (I/B/E/S) databases with an F-score over 0.94 using only a hundred of manually labeled company links.
The increasing size and dimensionality of real-world datasets make it necessary to design efficie... more The increasing size and dimensionality of real-world datasets make it necessary to design efficient algorithms not only in the training process but also in the prediction phase. In applications such as credit card fraud detection, the classifier needs to predict an event in 10 milliseconds at most. In these environments the speed of the prediction constraints heavily outweighs the training costs. We propose a new classification method, called a Hierarchical Linear Support Vector Machine (H-LSVM), based on the construction of an oblique decision tree in which the node split is obtained as a Linear Support Vector Machine. Although other methods have been proposed to break the data space down in subregions to speed up Support Vector Machines, the H-LSVM algorithm represents a very simple and efficient model in training but mainly in prediction for large-scale datasets. Only a few hyperplanes need to be evaluated in the prediction step, no kernel computation is required and the tree structure makes parallelization possible. In experiments with medium and large datasets, the H-LSVM reduces the prediction cost considerably while achieving classification results closer to the non-linear SVM than that of the linear case.
ABSTRACT A chemical detection system made of a gas sensor array and algorithms intended to monito... more ABSTRACT A chemical detection system made of a gas sensor array and algorithms intended to monitor human activity was tested in a NASA spacecraft cabin simulator. Such a chemical-based monitoring system, if extended to home settings, would allow the autonomous detection of emergency situations, thereby postponing the moving of elderly people to assisted living facilities and improving their quality of life. Moreover, in contrast to other monitoring systems based on wearable sensors or video cameras, a monitoring system based on measuring changes in air composition induced by human activities would be non-invasive and would not raise privacy concerns when installed in homes. The third generation of the JPL sensor array was adapted in a small, compact and portable system and deployed in a spacecraft-like room for four weeks while volunteers were performing daily routines. The system was able to predict the total number of people and the level of activity performed in the room, while detecting unexpectedly high concentrations of volatiles.
We reformulate the Quadratic Programming Feature Selection (QPFS) method in a kernel space to obt... more We reformulate the Quadratic Programming Feature Selection (QPFS) method in a kernel space to obtain a vector which maximizes the quadratic objective function of QPFS. We demonstrate that the vector obtained by Kernel Quadratic Programming Feature Selection is equivalent to the Kernel Fisher vector and, therefore, a new interpretation of the Kernel Fisher Discriminant Analysis is given which provides some computational advantages for highly unbalanced datasets.
In this paper, we analyze the performance of several well-known pattern recognition and dimension... more In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectro-grams from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple clas-sifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.
Chemometrics and Intelligent Laboratory Systems, 2014
ABSTRACT We investigate optimal experiment selection to train a classifier on gas sensor arrays t... more ABSTRACT We investigate optimal experiment selection to train a classifier on gas sensor arrays to get the maximal possible performance in a limited number of experiments. In gas sensing, while collecting data for a particular sensor array, one has to choose what gas and concentration level is going to be presented in the next experiment. It is an active decision by the operator selecting the experiments and training the classifiers. Can the algorithm be trained sooner rather than later? Can we minimize the costs of collecting the data in terms of the man-hour of the operator and the expenses of the experiment itself? Active control sampling provides a way to deal with the challenge of minimizing the calibration costs and is applicable to any situation where experimental selection is parameterized by an external control variable. Our results indicate that active sampling strategies can only improve a random selection of experiments over a wide range of concentration of gasses. However, random or uninformed selection is fairly close. Additionally, our active sampling methodology reveals that, when there is no prior knowledge about the range of concentrations to which the sensor will be exposed during real operation, sensor must be calibrated over the entire working range, not just high concentrations. In fact, our results show that it is especially important to include low concentrations in the calibration since the lack of these values would dramatically decrease the performance of the system.
Identifying a subset of features that preserves classification accuracy is a problem of growing i... more Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nyström method for approximate matrix diagonalization. QPFS is thus capable of dealing with very large data sets, for which the use of other methods is computationally expensive. In experiments with small and medium data sets, the QPFS method leads to classification accuracy similar to that of other successful techniques. For large data sets, QPFS is superior in terms of computational efficiency.
Proceedings of the Edinburgh Mathematical Society, Jul 18, 2014
The goal of this article is the study of homogeneous Riemannian structure tensors within the fram... more The goal of this article is the study of homogeneous Riemannian structure tensors within the framework of reduction under a group H of isometries. In a first result, H is a normal subgroup of the group of symmetries associated to the reducing tensorS. The situation when H is any group acting freely is analyzed in a second result. The invariant classes of homogeneous tensors are also investigated when reduction is performed. It turns out that the geometry of the fibres is involved in the preservation of some of them. Some classical examples illustrate the theory. Finally, the reduction procedure is applied to fiberings of almost contact manifolds over almost Hermitian manifolds. If the structure is moreover Sasakian, the obtained reduced tensor is homogeneous Kähler.
Rigorous and proper linking of financial databases is a necessary step to test trading strategies... more Rigorous and proper linking of financial databases is a necessary step to test trading strategies incorporating multimodal sources of information. This paper proposes a machine learning solution to match companies in heterogeneous financial databases. Our method, named Financial Attribute Selection Distance (FASD), has two stages, each of them corresponding to one of the two interrelated tasks commonly involved in heterogeneous database matching problems: schema matching and entity matching. FASD's schema matching procedure is based on the Kullback-Leibler divergence of string and numeric attributes. FASD's entity matching solution relies on learning a company distance flexible enough to deal with the numeric and string attribute links found by the schema matching algorithm, and it incorporates different string matching approaches such as edit-based and token-based metrics. The parameters of the distance are optimized using the F-score as cost function. FASD is able to match the joint Compustat/CRSP and Institutional Brokers' Estimate System (I/B/E/S) databases with an F-score over 0.94 using only a hundred of manually labeled company links.
The increasing size and dimensionality of real-world datasets make it necessary to design efficie... more The increasing size and dimensionality of real-world datasets make it necessary to design efficient algorithms not only in the training process but also in the prediction phase. In applications such as credit card fraud detection, the classifier needs to predict an event in 10 milliseconds at most. In these environments the speed of the prediction constraints heavily outweighs the training costs. We propose a new classification method, called a Hierarchical Linear Support Vector Machine (H-LSVM), based on the construction of an oblique decision tree in which the node split is obtained as a Linear Support Vector Machine. Although other methods have been proposed to break the data space down in subregions to speed up Support Vector Machines, the H-LSVM algorithm represents a very simple and efficient model in training but mainly in prediction for large-scale datasets. Only a few hyperplanes need to be evaluated in the prediction step, no kernel computation is required and the tree structure makes parallelization possible. In experiments with medium and large datasets, the H-LSVM reduces the prediction cost considerably while achieving classification results closer to the non-linear SVM than that of the linear case.
ABSTRACT A chemical detection system made of a gas sensor array and algorithms intended to monito... more ABSTRACT A chemical detection system made of a gas sensor array and algorithms intended to monitor human activity was tested in a NASA spacecraft cabin simulator. Such a chemical-based monitoring system, if extended to home settings, would allow the autonomous detection of emergency situations, thereby postponing the moving of elderly people to assisted living facilities and improving their quality of life. Moreover, in contrast to other monitoring systems based on wearable sensors or video cameras, a monitoring system based on measuring changes in air composition induced by human activities would be non-invasive and would not raise privacy concerns when installed in homes. The third generation of the JPL sensor array was adapted in a small, compact and portable system and deployed in a spacecraft-like room for four weeks while volunteers were performing daily routines. The system was able to predict the total number of people and the level of activity performed in the room, while detecting unexpectedly high concentrations of volatiles.
We reformulate the Quadratic Programming Feature Selection (QPFS) method in a kernel space to obt... more We reformulate the Quadratic Programming Feature Selection (QPFS) method in a kernel space to obtain a vector which maximizes the quadratic objective function of QPFS. We demonstrate that the vector obtained by Kernel Quadratic Programming Feature Selection is equivalent to the Kernel Fisher vector and, therefore, a new interpretation of the Kernel Fisher Discriminant Analysis is given which provides some computational advantages for highly unbalanced datasets.
In this paper, we analyze the performance of several well-known pattern recognition and dimension... more In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectro-grams from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple clas-sifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.
Chemometrics and Intelligent Laboratory Systems, 2014
ABSTRACT We investigate optimal experiment selection to train a classifier on gas sensor arrays t... more ABSTRACT We investigate optimal experiment selection to train a classifier on gas sensor arrays to get the maximal possible performance in a limited number of experiments. In gas sensing, while collecting data for a particular sensor array, one has to choose what gas and concentration level is going to be presented in the next experiment. It is an active decision by the operator selecting the experiments and training the classifiers. Can the algorithm be trained sooner rather than later? Can we minimize the costs of collecting the data in terms of the man-hour of the operator and the expenses of the experiment itself? Active control sampling provides a way to deal with the challenge of minimizing the calibration costs and is applicable to any situation where experimental selection is parameterized by an external control variable. Our results indicate that active sampling strategies can only improve a random selection of experiments over a wide range of concentration of gasses. However, random or uninformed selection is fairly close. Additionally, our active sampling methodology reveals that, when there is no prior knowledge about the range of concentrations to which the sensor will be exposed during real operation, sensor must be calibrated over the entire working range, not just high concentrations. In fact, our results show that it is especially important to include low concentrations in the calibration since the lack of these values would dramatically decrease the performance of the system.
Identifying a subset of features that preserves classification accuracy is a problem of growing i... more Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nyström method for approximate matrix diagonalization. QPFS is thus capable of dealing with very large data sets, for which the use of other methods is computationally expensive. In experiments with small and medium data sets, the QPFS method leads to classification accuracy similar to that of other successful techniques. For large data sets, QPFS is superior in terms of computational efficiency.
Proceedings of the Edinburgh Mathematical Society, Jul 18, 2014
The goal of this article is the study of homogeneous Riemannian structure tensors within the fram... more The goal of this article is the study of homogeneous Riemannian structure tensors within the framework of reduction under a group H of isometries. In a first result, H is a normal subgroup of the group of symmetries associated to the reducing tensorS. The situation when H is any group acting freely is analyzed in a second result. The invariant classes of homogeneous tensors are also investigated when reduction is performed. It turns out that the geometry of the fibres is involved in the preservation of some of them. Some classical examples illustrate the theory. Finally, the reduction procedure is applied to fiberings of almost contact manifolds over almost Hermitian manifolds. If the structure is moreover Sasakian, the obtained reduced tensor is homogeneous Kähler.
Uploads
Papers by I Lujan