Improving k-nn search and subspace clustering based on local intrinsic dimensionality

In several novel applications such as multimedia and recommender systems, data is often represented as object feature vectors in high-dimensional spaces. The high-dimensional data is always a challenge for state-of-the-art algorithms, because of the so-called curse of dimensionality . As the dimensionality increases, the discriminative ability of similarity measures diminishes to the point where many data analysis algorithms, such as similarity search and clustering, that depend on them lose their effectiveness. One way to handle this challenge is by selecting the most important features, which is essential for providing compact object representations as well as improving the overall search and clustering performance. Having compact feature vectors can further reduce the storage space and the computational complexity of search and learning tasks. Support-Weighted Intrinsic Dimensionality (support-weighted ID) is a new promising feature selection criterion that estimates the contribution of each feature to the overall intrinsic dimensionality. Support-weighted ID identifies relevant features locally for each object, and penalizes those features that have locally lower discriminative power as well as higher density. In fact, support-weighted ID measures the ability of each feature to locally discriminate between objects in the dataset. Based on support-weighted ID, this dissertation introduces three main research contributions: First, this dissertation proposes NNWID-Descent, a similarity graph construction method that utilizes the support-weighted ID criterion to identify and retain relevant features locally for each object and enhance the overall graph quality. Second, with the aim to improve the accuracy and performance of cluster analysis, this dissertation introduces k-LIDoids, a subspace clustering algorithm that extends the utility of support-weighted ID within a clustering framework in order to gradually select the subset of informative and important features per cluster. k-LIDoids is able to construct clusters together with finding a low dimensional subspace for each cluster. Finally, using the compact object and cluster representations from NNWID-Descent and k-LIDoids, this dissertation defines LID-Fingerprint, a new binary fingerprinting and multi-level indexing framework for the high-dimensional data. LID-Fingerprint can be used for hiding the information as a way of preventing passive adversaries as well as providing an efficient and secure similarity search and retrieval for the data stored on the cloud. When compared to other state-of-the-art algorithms, the good practical performance provides an evidence for the effectiveness of the proposed algorithms for the data in high-dimensional spaces

Query by Example of Speaker Audio Signals using Power Spectrum and MFCCs

Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%

Fast retrieval of weather analogues in a multi-petabyte meteorological archive

The European Centre for Medium-Range Weather Forecasts (ECMWF) manages the largest archive of meteorological data in the world. At the time of writing, it holds around 300 petabytes and grows at a rate of 1 petabyte per week. This archive is now mature, and contains valuable datasets such as several reanalyses, providing a consistent view of the weather over several decades. Weather analogue is the term used by meteorologists to refer to similar weather situations. Looking for analogues in an archive using a brute force approach requires data to be retrieved from tape and then compared to a user-provided weather pattern, using a chosen similarity measure. Such an operation would be very long and costly. In this work, a wavelet-based fingerprinting scheme is proposed to index all weather patterns from the archive, over a selected geographical domain. The system answers search queries by computing the fingerprint of the query pattern and looking for close matched in the index. Searches are fast enough that they are perceived as being instantaneous. A web-based application is provided, allowing users to express their queries interactively in a friendly and straightforward manner by sketching weather patterns directly in their web browser. Matching results are then presented as a series of weather maps, labelled with the date and time at which they occur. The system has been deployed as part of the Copernicus Climate Data Store and allows the retrieval of weather analogues from ERA5, a 40-years hourly reanalysis dataset. Some preliminary results of this work have been presented at the International Conference on Computational Science 2018 (Raoult et al. (2018))

Efficient and robust audio fingerprinting

Master'sMASTER OF SCIENC