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Feature selection is a key step in classification of high-dimensional data, especially gene expression microarray data with many thousands of features. As a wrapper method, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is one of the most powerful feature selection techniques. Although SVM-RFE can remove irrelevant features effectively, it cannot deal with most of the redundant features...
A method for spotting specific words in sign language video is proposed. In classes and talks given using Japanese Sign Language, words that do not have a defined sign, such as the names of people, objects, and places, are represented by sets of multiple characters from the Japanese finger alphabet. The difficulty of recognizing these words has created strong demand for the ability to spot specific...
In the study on sports image classification, the characteristics of human pose increasingly raise concerns of researchers. However, the same posture for human may be resulted from different scenes and scene objects that express diverse action states and meanings. Thus, combination of human pose and event scenes shall be considered so as to improve performance of sports image classification. In recent...
Short-range millimeter (mm) wave radar imaging has significant potential for emerging applications such as handheld-based gesture recognition and vehicular situational awareness. In this paper, we develop a theoretical framework for an array of monostatic elements for this purpose. We show that we must account for form factor and complexity constraints in a manner that is quite different from that...
The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous...
This paper considers measuring brain functional connectivity using mutual information (MI). First, we explain the advantage of MI based analysis over the conventional correlation based analysis. Second, we propose a novel approach for MI estimation by exploiting kernel-based probability density function (pdf) estimation and optimization under the maximum likelihood criteria. Finally, the proposed...
The work presents an algorithm for recognition of isolated airstrips in multiscale Google satellite images. First, strong straight lines are detected by Radon Transform and airstrip detection is accomplished by detecting longest straight lines in multiresolution images at different altitudes. Later normalized crosscorrelation is used to find the degree of similarity among multiscale airstrip patterns...
Human tracking in crowded scenes is a challenging problem because occlusion is frequently occurred. In this paper, we propose an online human tracking method which can handle occlusion effectively. Our method automatically changes a learning rate for updating tracking model according to the situation. If the tracking target is under occlusion, the learning rate decreases to reduce the influence of...
When the amount of training data is limited, the successful application of machine learning techniques typically hinges on the ability to identify useful features or abstractions. Expert knowledge often plays a crucial role in this feature engineering process. However, manual creation of such abstractions can be labor intensive and expensive. In this paper, we propose a feature learning framework...
This paper presents an adaptive progressive image acquisition algorithm based on the concept of kernel construction. The algorithm takes the conventional route of blind progressive sampling to sample and reconstruct the ground truth image in an iterative manner. During each iteration, an equivalent kernel is built for each unsampled pixel to capture the spatial structure of its local neighborhood...
This paper proposes a new scheme for the 2D-3D face recognition problem. Our proposed framework mainly consists of Restricted Boltzmann Machines (RBMs) and a correlation learning model. In the framework, a single-layer network based on RBMs is adopted to extract latent features over two different modalities. Furthermore, the latent hidden layer features of different models are projected to formulate...
Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data set obtained from high-throughput experiments is known to be noisy and incomplete. By modeling PPI data as a graph, research efforts are being made in the literature to improve the performance of protein function prediction by extending...
Robust scale calculation is a challenging problem in visual object tracking. Most state-of-the-art trackers fail to handle large scale variations in complex image sequences. This paper propose a novel approach for robust scale calculation in a tracking-by-detection framework. The proposed approach divides the target into four patches and computes the scale factor by finding the maximum response position...
Multi-output regression estimation aims at mining a vector-valued function from multi-dimensional input vector to multi-dimensional output vector. However, the output variables may be correlative. It is desirable to develop a multi-dimensional regression model taking advantage of the possible correlations. Therefore, this paper proposes a novel multi-output support vector regression model via double...
Parkinson is a disease attacking the nervous system and worsens the work of nervous system over time. This disease is incurable, the therapy existing today is only able to help to relieve the symptoms. Hence, an early diagnose is deemed essential to determine an accurate type of therapy. Parkinson disease can be diagnosed by examining the symptoms apparent to the patient. One of the symptoms is the...
We propose a new method derived from DACCER (Distributed Assessment of the Closeness CEntrality Ranking): the modified DACCER (MDACCER), for assessing traditional closeness centrality ranking. MDACCER presents a relaxation that allows it to take advantage of massively parallel environments like General Purpose Graphics Processing Units (GPGPUs). Traditional DACCER proposal assesses Closeness centrality...
In this paper we present a machine-learning approach to predict the total communication time of parallel applications. Communication time is heavily dependent on a very wide set of parameters relevant to the architecture, runtime configuration and application communication profile. We focus our study on parameters that can be easily extracted from the application and the process mapping ahead of execution...
Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep learning? This paper proposes to introduce...
Inspite of its complete foundation, Ayurveda has not received acknowledgement in the scientific world. This may be due to absence of taking an initiative in its experimental research. In this paper we have emphasized on the Ayurvedic constituent ‘Pitta’, by examining the features extracted from the Acceleration Plethysmography, to design and authenticate a high Pitta classifier. For this objective,...
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