Robust and accurate cancer classification is critical in cancer treatment. Gene expression profiling is expected to enable us to diagnose tumors precisely and systematically. However, the classification task in this c...
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ISBN:
(纸本)0769523447
Robust and accurate cancer classification is critical in cancer treatment. Gene expression profiling is expected to enable us to diagnose tumors precisely and systematically. However, the classification task in this context is very challenging because of the curse of dimensionality and the small sample size problem. In this paper, we propose a novel method to solve these two problems. Our method is able to map gene expression data into a very low dimensional space and thus meets the recommended samples to features per class ratio. As a result, it can be used to classify new samples robustly with low and trustable (estimated) error rates. The method is based on linear discriminant analysis (LDA). However, the conventional LDA requires that the within-class scatter matrix S/sub w/ be nonsingular. Unfortunately, S/sub w/ is always singular in the case of cancer classification due to the small sample size problem. To overcome this problem, we develop a generalized linear discriminant analysis (GLDA) that is a general, direct, and complete solution to optimize Fisher's criterion. GLDA is mathematically well-founded and coincides with the conventional LDA when S/sub w/ is nonsingular. Different from the conventional LDA, GLDA does not assume the nonsingularity of S/sub w/, and thus naturally solves the small sample size problem. To accommodate the high dimensionality of scatter matrices, a fast algorithm of GLDA is also developed. Our extensive experiments on seven public cancer datasets show that the method performs well. Especially on some difficult instances that have very small samples to genes per class ratios, our method achieves much higher accuracies than widely used classification methods such as support vector machines, random forests, etc.
Interest in context-aware computing has expanded the use of sensing technologies. The accelerometer is one of the most widely used sensors for capturing context because it is small, inexpensive, lightweight, and self-...
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ISBN:
(纸本)1595930892
Interest in context-aware computing has expanded the use of sensing technologies. The accelerometer is one of the most widely used sensors for capturing context because it is small, inexpensive, lightweight, and self-operable. In efforts to obtain behavioral patterns, many studies have reported the use of multiple accelerometers attached to the human body. However, this is difficult to implement in real-life situations and may not fully address the context of user interaction. In contrast, the present study employed a single tri-axial accelerometer attached to a handheld computing device instead of to a user. The objective was to determine what contextual information could be obtained from this more feasible, albeit limited, source of acceleration data. Data analyses confirmed that changes in both mobility and lighting conditions induced statistically significant differences in the output of the accelerometer. Copyright 2005 ACM.
In this paper, we argue firstly that researchers in critical computing should address the specific information and communication technology (ICT) needs and activities of those agencies concerned with emancipatory issu...
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Based on the triangulation method, the 3D motion of an object can be completely recognized by a stereo camera. However, the question whether or not the 3D motion of an object can be completely recognized by a motionle...
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Based on the triangulation method, the 3D motion of an object can be completely recognized by a stereo camera. However, the question whether or not the 3D motion of an object can be completely recognized by a motionless / fixed monocular camera is the yet-unanswered question. In this paper we propose a method using a motionless monocular camera of which the focus is changed in cycle to recognize the absolute 3D motion of an object. We name the method motion from focus.
We introduce an annotation-based rescue assistance system for a teleoperated unmanned helicopter with an wearable augmented reality (AR) environment. In this system, an operator controls the helicopter remotely while ...
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We introduce an annotation-based rescue assistance system for a teleoperated unmanned helicopter with an wearable augmented reality (AR) environment. In this system, an operator controls the helicopter remotely while watching an annotated view from the helicopter through a head mounted display (HMD) with a laptop PC in a backpack. Virtual buildings and textual annotations assist the rescue operation indicating the position to search rapidly and intensively. The position and the attitude of the helicopter is measured by a GPS and a gyroscope, and sent to the operator's PC via a wireless LAN. Using this system, we conducted experiments to find persons and verified the feasibility.
Advanced driver assistance systems are designed to make driving easier that is, to alleviate the driver's workload, and to increase traffic safety. However, traffic safety is affected by negative behavioral adapta...
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Advanced driver assistance systems are designed to make driving easier that is, to alleviate the driver's workload, and to increase traffic safety. However, traffic safety is affected by negative behavioral adaptation, meaning that drivers tend to increase speed and pay less attention to driving when supported by an advanced assistance system. We relate behavioral adaptation to reinforcement learning at a subconscious level, and propose that driver assistance is dynamically varied within predetermined safety limits. The aim of employing a dynamic assistance policy is to prevent the driver from noticing a constant improvement in vehicle handling. We conclude by describing ongoing work for empirically evaluating an improved lane departure warning system that uses a dynamic assistance policy.
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