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As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. Similarity search, as a basic operator for many machine learning and data mining algorithms, has been extensively studied before, leading to several efficient solutions...
Lasso simultaneously conducts variable selection and supervised regression. In this paper, we extend Lasso to multiple output prediction, which belongs to the categories of structured learning. Though structured learning makes use of both input and output simultaneously, the joint feature mapping in current framework of structured learning is usually application-specific. As a result, ad hoc heuristics...
In this paper, we present a rich image representation which is robust to illumination, facial expression and scale variations. For this aim, firstly, we propose a novel dense local image representation method based on Walsh Hadamard Transform (WHT) called Local WHT (LWHT). LWHT is the application of WHT to each pixel of an image to decompose it into multiple components, called LWHT maps. Secondly,...
Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an efficient and scalable MIL algorithm named miFV. Our algorithm maps the original MIL bags into a new feature...
Anomaly detection starts from a model of normalbehavior and classifies departures from this model as anomalies. This paper introduces a statistical non-parametric approach for anomaly detection that is based on a multivariate extension of the Poisson point process model for univariateextremes. The method is demonstrated on both a synthetic and a real-world data set, the latter being an unbalanced...
In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is...
We propose an approach suitable to learn multiple time-varying models jointly and discuss an application in data-driven weather forecasting. The methodology relies on spectral regularization and encodes the typical multi-task learning assumption that models lie near a common low dimensional subspace. The arising optimization problem amounts to estimating a matrix from noisy linear measurements within...
Food-related photos have become increasingly very popular, due to social networks, food recommendation and dietary assessment systems. Reliable annotation is essential in those systems, but user-contributed tags are often non-informative and inconsistent, and unconstrained automatic food recognition still has relatively low accuracy. Most works focus on exploiting only the visual content while ignoring...
This paper employs sparse Bayesian approach to enable the Probabilistic Classification Vector Machine (PCVM) to select a relevant subset of features. Because of probabilistic outputs and the ability to automatically optimize the regularization items, the sparse Bayesian framework has shown great advantages in real-world applications. However, the Gaussian priors that introduce the same prior to different...
The diffusion least mean squares (LMS) [1] algorithm gives faster convergence than the original LMS in a distributed network. Also, it outperforms other distributed LMS algorithms like spatial LMS and incremental LMS [2]. However, both LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable [3]. A variant of LMS called kernel-LMS (KLMS) has been...
Malware writers use increasingly complex evasion mechanisms to ensure the concealment of malware against standard anti-malware suites. To identify malware through its behaviour, rather than its approach is an interesting venue of exploration. System call traces are highly indicative of a process behaviour. However, it is difficult to acquire system calls of all processes running on a physical machine...
Chord represents the back-bone of occidental music genre as it contains rich harmonic information which is useful for various music applications such as music genre classification or music retrieval. Hence, chord recognition or transcription is of importance for music representation. In this paper we focus on chord recognition and especially investigate different features representation used in such...
Many statistical learning tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomenon is often an obstacle to the use of many methods for solving these tasks. To avoid this phenomenon, various dimensionality reduction algorithms are used as the first key step in solving these tasks. The algorithms transform original high-dimensional data into lower...
The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor...
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
Full use of the parallel computation capabilities of present and expected CPUs and CPUs require use of vector extensions. Yet many actors in data flow systems for digital signal processing have internal state (or, equivalently, an edge that loops from the actor back to itself) that impose serial dependencies between actor invocations that make vectorizing across actor invocations impossible. Ideally,...
Eigenface is one of the most common appearance based approaches for face recognition. Eigenfaces are the principal components which represent the training faces. Using Principal Component Analysis, each face is represented by very few parameters called weight vectors or feature vectors. While this makes testing process easy, it also includes cumbersome process of generating eigenspace and projecting...
GPU has become a first-order computing plat-form. Nonetheless, not many performance modeling techniques have been developed for architecture studies. Several GPU analytical performance models have been proposed, but they mostly target application optimizations rather than the study of different architecture design options. Interval analysis is a relatively accurate performance modeling technique,...
In this paper, we propose a new approach to data density estimation based on the total sum of distances from a data point, and the recently introduced Recursive Density Estimation technique. It is suitable for autonomous real-time video analytics problems, and has been specifically designed to be executed very fast; it uses integer-only arithmetic with no divisions and no floating point numbers (no...
Compressive sensing is the theory of sparse signal recovery from undersampled measurements or observations. Exact signal reconstruction is an NP hard problem. A convex approximation using the l1-norm has received a great deal of theoretical attention. Exact recovery using the l1 approximation is only possible under strict conditions on the measurement matrix, which are difficult to check. Many greedy...
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