Video summarization remains a challenging task despite increasing research efforts. Traditional methods focus solely on long-range temporal modeling of video frames, overlooking important local motion information that cannot be captured by frame-level ...

Marginal fisher analysis is an effective feature extraction algorithm for face recognition, but the algorithm is sensitive to the influence of the neighbourhood parameter setting, and does not have the function of feature selection. In order to solve the ...

With the fast-growing multi-modal/media data in the Big Data era, multi-view clustering (MVC) has attracted lots of attentions lately. Most MVCs focus on integrating and utilizing the complementary information among views by linear sum of the learned ...

Can we find heterogeneous clusters hidden in data sets with 80% noise? Although such settings occur in the real-world, we struggle to find methods from the abundance of clustering techniques that perform well with noise at this level. Indeed, perhaps ...

In this paper, we present Δ-SPOT, a non-linear model for analysing large scale web search data, and its fitting algorithm. Δ-SPOT can forecast long-range future dynamics of the keywords/queries. We use the Google Search, Twitter and MemeTracker data set ...

Given a large collection of time-evolving activities, such as Google search queries, which consist of d keywords/activities for m locations of duration n, how can we analyze temporal patterns and relationships among all these activities and find ...

Given a large collection of co-evolving online activities, such as searches for the keywords "Xbox", "PlayStation" and "Wii", how can we find patterns and rules? Are these keywords related? If so, are they competing against each other? Can we forecast ...

The ILU-based preconditioning methods in previous work have their own parameters to improve their performances. Although the parameters may degrade the performance, their determination is left to users. Thus, these previous methods are not reliable in ...

This paper proposes an approach of hierarchical image data organization for effective image retrieval. Our approach is basically based on the Cross-Association (CA) that was originally devised for uncovering hidden communities in data without requiring ...

We consider analysis of noisy and incomplete hyperspectral imagery, with the objective of removing the noise and inferring the missing data. The noise statistics may be wavelength dependent, and the fraction of data missing (at random) may be ...

This paper proposes an adaptive step-size method for blind source separation (BSS) suitable for robot audition systems. The design of the step-size parameter is a critical consideration when we apply BSS to real-world applications such as robot audition ...

As a fundamental problem in data mining, pattern recognition and machine learning, clustering algorithm has been studied for decades, and has been improved in many aspects. However, parameter-free clustering algorithms are still quite weak, which makes ...

This paper proposes a parameter-free classifier which combines K-means with Nearest Neighbor Rule (NNR) - called Incremental Cluster-based Classification (ICC). The classifier is used in low power and capacity devices such as Personal Digital Assistant (...