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Nearest neighbor based feature selection for regression and its application to neural activity

Nearest neighbor based feature selection for regression and ...

引用

2005 Annual Conference on neural Information Processing Systems, NIPS 2005

作者： Navot, Amir Shpigelman, Lavi Tishby, Naftali Vaadia, Eilon School of Computer Science and Engineering Hadassah Medical School Hebrew University Jerusalem 91904 Israel Interdisciplinary Center for Neural Computation Hadassah Medical School Hebrew University Jerusalem 91904 Israel Dept. of Physiology Hadassah Medical School Hebrew University Jerusalem 91904 Israel

ISBN: (纸本)9780262232531

We present a non-linear, simple, yet effective, feature subset selection method for regression and use it in analyzing cortical neural activity. Our algorithm involves a feature-weighted version of the k-nearest-neighbor algorithm. It is able to capture complex dependency of the target function on its input and makes use of the leave-one-out error as a natural regularization. We explain the characteristics of our algorithm on synthetic problems and use it in the context of predicting hand velocity from spikes recorded in motor cortex of a behaving monkey. By applying feature selection we are able to improve prediction quality and suggest a novel way of exploring neural data.

关键词： Feature Selection

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Query By Committee made real 05

Query By Committee made real

引用

Proceedings of the 19th International Conference on neural Information Processing Systems

作者： Ran Gilad-Bachrach Amir Navot Naftali Tishby School of Computer Science and Engineering The Hebrew University Jerusalem Israel and Intel Research Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel School of Computer Science and Engineering The Hebrew University Jerusalem Israel and Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel

Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost. In this paper we introduce a new algorithm, KQBC, which is capable of actively learning large scale problems by using selective sampling. The algorithm overcomes the costly sampling step of the well known Query By Committee (QBC) algorithm by projecting onto a low dimensional space. KQBC also enables the use of kernels, providing a simple way of extending QBC to the non-linear scenario. Sampling the low dimension space is done using the hit and run random walk. We demonstrate the success of this novel algorithm by applying it to both artificial and a real world problems.

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Noise and the two-thirds power law

Noise and the two-thirds power law

引用

2005 Annual Conference on neural Information Processing Systems, NIPS 2005

作者： Maoz, Uri Portugaly, Elon Flash, Tamar Weiss, Yair Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Edmond Safra Campus Givat Ram Jerusalem 91904 Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science PO Box 26 Rehovot 76100 Israel School of Computer Science and Engineering Hebrew University of Jerusalem Edmond Safra Campus Givat Ram Jerusalem 91904 Israel

ISBN: (纸本)9780262232531

The two-thirds power law, an empirical law stating an inverse non-linear relationship between the tangential hand speed and the curvature of its trajectory during curved motion, is widely acknowledged to be an invariant of upper-limb movement. It has also been shown to exist in eyemotion, locomotion and was even demonstrated in motion perception and prediction. This ubiquity has fostered various attempts to uncover the origins of this empirical relationship. In these it was generally attributed either to smoothness in hand- or joint-space or to the result of mechanisms that damp noise inherent in the motor system to produce the smooth trajectories evident in healthy human motion. We show here that white Gaussian noise also obeys this power-law. Analysis of signal and noise combinations shows that trajectories that were synthetically created not to comply with the power-law are transformed to power-law compliant ones after combination with low levels of noise. Furthermore, there exist colored noise types that drive non-power-law trajectories to power-law compliance and are not affected by smoothing. These results suggest caution when running experiments aimed at verifying the power-law or assuming its underlying existence without proper analysis of the noise. Our results could also suggest that the power-law might be derived not from smoothness or smoothness-inducing mechanisms operating on the noise inherent in our motor system but rather from the correlated noise which is inherent in this motor system.

关键词： Trajectories

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Metric learning by collapsing classes 05

Metric learning by collapsing classes

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Proceedings of the 19th International Conference on neural Information Processing Systems

作者： Amir Globerson Sam Roweis School of Computer Science and Engineering Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel Machine Learning Group Department of Computer Science University of Toronto Canada

We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one under which points in the same class are simultaneously near each other and far from points in the other classes. We construct a convex optimization problem whose solution generates such a metric by trying to collapse all examples in the same class to a single point and push examples in other classes infinitely far away. We show that when the metric we learn is used in simple classifiers, it yields substantial improvements over standard alternatives on a variety of problems. We also discuss how the learned metric may be used to obtain a compact low dimensional feature representation of the original input space, allowing more efficient classification with very little reduction in performance.

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Analyzing auditory neurons by learning distance functions 05

Analyzing auditory neurons by learning distance functions

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Proceedings of the 19th International Conference on neural Information Processing Systems

作者： Inna Weiner Tomer Hertz Israel Nelken Daphna Weinshall School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel and The Center for Neural Computation The Hebrew University of Jerusalem Jerusalem Israel The Center for Neural Computation The Hebrew University of Jerusalem Jerusalem Israel and Department of Neurobiology The Hebrew University of Jerusalem Jerusalem Israel

We present a novel approach to the characterization of complex sensory neurons. One of the main goals of characterizing sensory neurons is to characterize dimensions in stimulus space to which the neurons are highly sensitive (causing large gradients in the neural responses) or alternatively dimensions in stimulus space to which the neuronal response are invariant (defining iso-response manifolds). We formulate this problem as that of learning a geometry on stimulus space that is compatible with the neural responses: the distance between stimuli should be large when the responses they evoke are very different, and small when the responses they evoke are similar. Here we show how to successfully train such distance functions using rather limited amount of information. The data consisted of the responses of neurons in primary auditory cortex (A1) of anesthetized cats to 32 stimuli derived from natural sounds. For each neuron, a subset of all pairs of stimuli was selected such that the responses of the two stimuli in a pair were either very similar or very dissimilar. The distance function was trained to fit these constraints. The resulting distance functions generalized to predict the distances between the responses of a test stimulus and the trained stimuli.

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Nearest neighbor based feature selection for regression and its application to neural activity 05

Nearest neighbor based feature selection for regression and ...

引用

Proceedings of the 19th International Conference on neural Information Processing Systems

作者： Amir Navot Lavi Shpigelman Naftali Tishby Eilon Vaadia School of computer Science and Engineering The Hebrew University Jerusalem Israel and Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel and Dept. of Physiology Hadassah Medical School The Hebrew University Jerusalem Israel

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Noise and the two-thirds power law 05

Noise and the two-thirds power law

引用

Proceedings of the 19th International Conference on neural Information Processing Systems

作者： Uri Maoz Elon Portugaly Tamar Flash Yair Weiss Interdisciplinary Center for Neural Computation The Hebrew Univ. of Jerusalem Jerusalem Israel and Dept. of Computer Science and Applied Mathematics The Weizmann Inst. of Science Rehovot Israel and School of Comp. Science and Eng. The Hebrew Univ. of Jerusalem Jerusalem Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel 2 Department of Computer Science and Applied Mathematics The Weizmann Institute of Science Rehovot Israel Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem Jerusalem Israel and School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel

The two-thirds power law, an empirical law stating an inverse non-linear relationship between the tangential hand speed and the curvature of its trajectory during curved motion, is widely acknowledged to be an invariant of upper-limb movement. It has also been shown to exist in eye-motion, locomotion and was even demonstrated in motion perception and prediction. This ubiquity has fostered various attempts to uncover the origins of this empirical relationship. In these it was generally attributed either to smoothness in hand- or joint-space or to the result of mechanisms that damp noise inherent in the motor system to produce the smooth trajectories evident in healthy human *** show here that white Gaussian noise also obeys this power-law. Analysis of signal and noise combinations shows that trajectories that were synthetically created not to comply with the power-law are transformed to power-law compliant ones after combination with low levels of noise. Furthermore, there exist colored noise types that drive non-power-law trajectories to power-law compliance and are not affected by smoothing. These results suggest caution when running experiments aimed at verifying the power-law or assuming its underlying existence without proper analysis of the noise. Our results could also suggest that the power-law might be derived not from smoothness or smoothness-inducing mechanisms operating on the noise inherent in our motor system but rather from the correlated noise which is inherent in this motor system.

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Passwords you'll never forget, but can't recall

Passwords you'll never forget, but can't recall

引用

Conference on Human Factors in Computing Systems, CHI EA 2004

作者： Weinshall, Daphna Kirkpatrick, Scott School of Computer Science and Engineering Center for Neural Computation Hebrew University Jerusalem Israel School of Engineering and Computer Science Hebrew University Jerusalem Israel

ISBN: (纸本)1581137036

We identify a wide range of human memory phenomena as potential certificates of identity. These "imprinting" behaviors are characterized by vast capacity for complex experiences, which can be recognized without apparent effort and yet cannot be transferred to others. They are suitable for use in near zero-knowledge protocols, which minimize the amount of secret information exposed to prying eyes while identifying an individual. We sketch several examples of such phenomena[1-3], and apply them in secure certification protocols. This provides a novel approach to human-computer interfaces, and raises new questions in several classic areas of psychology. © 2004 ACM.

关键词： Authentication

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Learning from a small number of training examples by exploiting object categories

Learning from a small number of training examples by exploit...

引用

2004 IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2004

作者： Levi, Kobi Fink, Michael Weiss, Yair School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem91904 Israel Center for Neural Computation Hebrew University of Jerusalem Jerusalem91904 Israel

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains. © 2004 IEEE.

关键词： Object detection

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Learning distance functions for image retrieval

Learning distance functions for image retrieval

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： T. Hertz A. Bar-Hillel D. Weinshall School of Computer Science and Engineering and the Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest learning such distance functions by training binary classifiers with margins, where the classifiers are defined over the product space of pairs of images. The classifiers are trained to distinguish between pairs in which the images are from the same class and pairs, which contain images from different classes. The signed margin is used as a distance function. We explore several variants of this idea, based on using SVM and boosting algorithms as product space classifiers. Our main contribution is a distance learning method, which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. The weak learner used is a Gaussian mixture model computed using a constrained EM algorithm, where the constraints are equivalence constraints on pairs of data points. This approach allows us to incorporate unlabeled data into the training process. Using some benchmark databases from the UCI repository, we show that our margin based methods significantly outperform existing metric learning methods, which are based an learning a Mahalanobis distance. We then show comparative results of image retrieval in a distributed learning paradigm, using two databases: a large database of facial images (YaleB), and a database of natural images taken from a commercial CD. In both cases our GMM based boosting method outperforms all other methods, and its generalization to unseen classes is superior.

关键词： Image retrieval Image databases Boosting Distributed databases Information retrieval Partitioning algorithms Support vector machines Support vector machine classification computer aided instruction Spatial databases

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