Due to the increasing demand for multivariate data analysis from the various application the dimensionality reduction becomes an important task to represent the data in low dimensional space for the robust data representation. In this paper, multivariate data analyzed by using a new approach SVM and ICA to enhance the classification accuracy in a way that data can be present in more condensed f...

Classification of Images is one of the challenging tasks in image analysis. Image classification is used in many fields such as Remote sensing, medical diagnosis, robotics, etc. Classification is to identify homogeneous groups of data points in a given dataset and assigning it to a class. In this paper classes of image objects are to be classified as region or area of interest for the land use/...

This paper describes the development of a 1-km landcover dataset of China by using monthly NDVI data spanning April 1992 through March 1993. The method used combined unsupervised and supervised classification of NDVI data from Ž . AVHRR. It is composed of five steps: a unsupervised clustering of monthly AVHRR NDVI maximum value composites is Ž . performed using the ISOCLASS algorithm; b prelimi...

Several techniques exist for remote sensing (RS) image classification, which includes supervised and unsupervised approaches. Classified maps are the main product of remote sensing image classification. Accuracy assessment of these classified maps is one of the foremost and important tasks of RS image classification technique. Without accuracy assessment the quality of map or output produced wo...

Supervised and unsupervised classification algorithms are the two main branches of machine learning [...]

The increasing scale and sophistication of network attacks have become a major concern for organizations around the world. As result, there is an demand effective accurate classification to enhance cyber security measures. Most existing schemes assume that available training data labeled; is, based on supervised learning. However, this not always case since real expected be unlabeled. In paper,...

Object localization aims to generate a tight bounding box for the target object, which is challenging problem that has been deeply studied in recent years. Since collecting bounding-box labels time-consuming and laborious, many researchers focus on weakly supervised object (WSOL). As appealing self-supervised learning technique shows its powerful function visual tasks, this paper, we take early...

We apply the framework of kernel dimension reduction, originally designed for supervised problems, to unsupervised dimensionality reduction. In this framework, kernel-based measures of independence are used to derive low-dimensional representations that maximally capture information in covariates in order to predict responses. We extend this idea and develop similarly motivated measures for uns...

We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ensemble approach to learn latent features of users. Combined with traditional ha...

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characteri...