One of the major problems of K-means is that one must use dense vectors for its centroids, and therefore it is infeasible to store such huge vectors in memory when the feature space is high-dimensional. We address this issue by using feature hashing (Weinberger et al., 2009), a dimension-reduction technique, which can reduce the size of dense vectors while retaining sparsity of sparse vectors. ...

A software package developed for the purpose of feature selection in statistical pattern recognition is presented. The software tool includes both several classical and new methods suitable for dimensionality reduction, classi.cation and data representation. Examples of solved problems are given, as well as observations regarding the behavior of criterion functions. ? 2002 Pattern Recognition S...

Video-based facial expression recognition (FER) has recently received increased attention as a result of its widespread application. Many kinds of features have been proposed to represent different properties of facial expressions in videos. However the dimensionality of these features is usually high. In addition, due to the complexity of the information available in video sequences, using onl...

We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform f...

A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) microand nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account, we have a dataset in hand with three hundred input features. Therefore, a p...

Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This work introduces a feature reduction method based on the singular value decomposition (SVD). This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery ...

In this paper we have focused on an efficient feature selection method in classification of audio files. The main objective is feature selection and extraction. We have selected a set of features for further analysis, which represents the elements in feature vector. By extraction method we can compute a numerical representation that can be used to characterize the audio using the existing toolb...

Annotation: Pattern recognition problem is briefly characterized as a process of machine learning. Its main stages (dimensionality reduction and classifier design) are stated. Statistical approach is given priority here. Two approaches to dimensionality reduction, namely feature selection (FS) and feature extraction (FE) are specified. Though FS is a special case of FE, they are very different ...

Feature selection, an important task in text categorization, is used for the purpose of dimensionality reduction. Feature selection basically can be performed locally and globally. For local selection, distinct feature sets are derived from different classes. The number of feature set is thus depended on the number of class. In contrary, only one universal feature set will be used in global fea...

In this paper we propose a new combined approach to feature space decomposition to improve the efficiency of the nonlinear dimensionality reduction method. The approach performs the decomposition of the original multidimensional space, taking into account the configuration of objects in the target low-dimensional space. The proposed approach is compared to the approach using hierarchical cluste...