Multi-view clustering is an important research topic due to its capability utilize complementary information from multiple views. However, there are few methods consider the negative impact caused by certain views with unclear structures, resulting in poor multi-view performance. To address this drawback, we propose self-supervised discriminative feature learning for deep m...

The effect of the choice of features on unsupervised clustering in audio surveillance is investigated. The importance of individual features in a larger feature set is first analyzed by examining the component loadings in principal component analysis (PCA). The individual sound events are then assigned into clusters using the self-tuning spectral clustering and the classical K-means algorithms....

We present CLIFF, an algorithm for clustering biological samples using gene expression microarray data. This clustering problem is difficult for several reasons, in particular the sparsity of the data, the high dimensionality of the feature (gene) space, and the fact that many features are irrelevant or redundant. Our algorithm iterates between two computational processes, feature filtering and...

Owning to the great growth of e-learning objects, authorities (e.g. ADL and IEEE) have developed some metadata standards to facilitate the keyword search for various e-learning applications. However, too much fields, such as 58 blank fields in IEEE LOM, waiting for authors or annotators to fill up become an endless nightmare. In order to reach our vision of sharing and reusing valuable assets, ...

Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information a...

The fuzzy c-means (FCM) algorithm is a popular fuzzy clustering method. It is known that an appropriate assignment to feature weights can improve the performance of FCM. In this paper, we use the bootstrap method proposed by Efron [Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Ann. Statist. 7, 1–26] to select feature weights based on statistical variations in the data. It i...

Deep clustering learns deep feature representations that favor clustering task using neural networks. Some pioneering work proposes to simultaneously learn embedded features and perform clustering by explicitly defining a clustering oriented loss. Though promising performance has been demonstrated in various applications, we observe that a vital ingredient has been overlooked by these work that...

This paper has a further exploration and study of visual feature extraction. Image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimens...

Gene expressions by microarray data technique have been effectively utilized for classification and diagnostic of cancer nodules. Numerous data mining techniques like clustering are presently applied for identifying cancer using gene expression data. An unsupervised learning technique is a clustering technique used to find out grouping structure in a set of data. The problem of feature selectio...

Extreme learning machine (ELM), used for the “generalized” single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of th...