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Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but… Expand A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to… Expand Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar… Expand We propose a novel method of dimensionality reduction for supervised learning problems. Given a regression or classification… Expand Advances in data collection and storage capabilities during the past decades have led to an information overload in most sciences… Expand Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because… Expand Abstract. The problem of similarity search in large time series databases has attracted much attention recently. It is a non… Expand Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and… Expand Abstract : We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software… Expand Abstract Modern advances in computing power have greatly widened scientists' scope in gathering and investigating information… Expand