We present a machine learning technique aimed at detecting abrupt changes in a sequence of vectors. Our algorithm requires a Mercer kernel together with the corresponding feature space. A stationarity index is designed in the feature space, and consists of comparing two circles corresponding to two -SV novelty detectors via a Fisher-like ratio. An abrupt change corresponds to a large distance b...

We consider the problem of retrieving sentence level restatements. Formally, we define restatements as sentences that contain all or some subset of information present in a query sentence. Identifying restatements is useful for several applications such as multi-document summarization, document provenance, text reuse and novelty detection. Spurious partial matches and term dependence become imp...

While people have many ideas about how a smart home should react to particular behaviours from their inhabitant, there seems to have been relatively little attempt to organise this systematically. In this paper, we attempt to rectify this in consideration of context awareness and novelty detection for a smart home that monitors its inhabitant for illness and unexpected behaviour. We do this thr...

In today’s world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to “Research Articles”. A method of identifying novelty at each sections of the article is highly required for determining the nov...

We address the problem of novelty detection in multiclass scenarios where some class labels are missing from the training set. Our method is based on the initial assignment of confidence values, which measure the affinity between a new test point and each known class. We first compare the values of the two top elements in this vector of confidence values. In the heart of our method lies the tra...

This paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approach aims to cover some of the challenges presented when condition monitoring is applied under a co...

We propose to use learning vector quantization (LVQ) in novelty detection where a few outliers exist in training data. The codebook update of original LVQ is modified and the scheme to determine a threshold for each codebook is proposed. Experimental results on artificial and real-world problems are quite promising.

In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year to support our experiments into large s...

We propose progressive minimal criteria novelty search (PMCNS), which is an extension of minimal criteria novelty search. In PMCNS, we combine the respective benefits of novelty search and fitnessbased evolution by letting novelty search freely explore new regions of behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our appr...

With the abundance of raw text documents available on the internet, many articles contain redundant information. Novel sentence mining can discover novel, yet relevant, sentences given a specific topic defined by a user. In real-time novelty mining, an important issue is to how to select a suitable novelty metric that quantitatively measures the novelty of a particular sentence. To utilize the ...