This paper reconsiders the aliasing problem of identifying the parameters of a continuous time stochastic process from discrete time data. It analyzes the extent to which restricting attention to processes with rational spectral density matrices reduces the number of observationally equivalent models. It...
Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used ... G Lee,DE Romo Bucheli,A Madabhushi - 《Plos One》 被引量...
Suppose the p-variate random vector W, partitioned into q variables W-1 and p - q variables W-2, follows a multivariate normal mixture distribution. If the investigator is mainly interested in estimation of the parameters of the distribution of W-1, there are two possibilities: (1) use ...
The intrinsic dimensionality is, in essence, a local characteristic of the distribution, as shown in Fig. 6-4. If we establish small local regions around X1, X2, X3, etc., the dimensionality within the local region must be close to 1 [19],[20]. Because of this, the intrinsic dimensio...
of the considered dynamical system, we demonstrate that reduction of dimensionality of x ( t ) by using its principal components in choosing the optimal control u ( t ) is necessary to overcome the computational difficulties that might arise in obtaining a solution of the optimal control problem...
Thermal resolution (also referred to as temperature uncertainty) establishes the minimum discernible temperature change sensed by luminescent thermometers and is a key figure of merit to rank them. Much has been done to minimize its value via probe optim
The problem of statistics and aggregate maintenance over data streams has gained popularity in recent years, especially in telecommunication network monitoring, web-click streams, stock tickers and other time- variant data. The amount of data generated in such applications can become too large to stor...
N Sochen,R Kimmel,R Malladi - 《IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society》 被引量: 1166发表: 1998年 Geometrical methods in statistics. We introduce a new geometrical framework based on which natural flows for image scale space and enhancement are...
LEB Barrios - 《Statistics & Computing》 被引量: 44发表: 2009年 Variants of Principal Components Analysis Principal components analysis (PCA) is probably the most commonly used transform to perform various tasks in many applications. It produces a set of uncorr... WM Liu,CI Chang - IEEE 被引...
Reducing the dimensionality of a dataset is an important and often challenging task. This can be done by either reducing the number of features, a task called feature selection, or by reducing the number of patterns, called data reduction. In this paper we propose methods that employ a novel...