Subspaces offer convenient means of representing information in many Pattern Recognition, Machine Vision, and Statistical Learning applications. Contrary to the growing popularity of subspace representations, the problem of efficiently searching through large subspace databases has received little attention in the past. In this paper we present a general solution to the Approximate Nearest Subs...

This paper proposes a method of reducing dimensions based on genetic algorithm and object-oriented classification based on support vector machine (SVM). The basic idea is subspace decomposition of hyperspectral images at first, then selecting suitable bands in each subspace by using genetic algorithm and putting all selected bands of each subspace together. Furthermore, the hyperspectral image ...

Band selection is one of the main methods reducing number dimensions in a hyperspectral image. Recently, various have been proposed to address this issue. However, these usually obtain band subset perspective locally optimal solution. To achieve an solution with global perspective, paper developed novel method for via combination strategy (OCS). The contributions are as follows: (1) subspace pa...

The speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. This paper discusses a novel technique for speech enhancement which is based on Singular Value Decomposition. This implementation utilizes a Genetic Algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

the speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. this paper discusses a novel technique for speech enhancement which is based on singular value decomposition. this implementation utilizes a genetic algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived.

We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of low-dimensional subspaces. The subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to their underlying low-dimensional subspaces....

Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived.