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

Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports

Continuous Descent Approach (CDA) is the flight technique for aircraft to continuously descend from cruise altitude with an idle thrust setting and without level-offs, contrary to the staircase-like Step-down Descent Approach (SDA). Important for air transportation sustainability, using CDA reduces noise, fuel consumption, and pollution. Nevertheless, CDA has been limited to low traffic levels at airports, often at night, because it requires more separation distance between aircraft arrivals and, thus, could decrease throughput. Insufficient attention has been given to helping air traffic controllers decide when CDA may be used. In this paper, we calculate the probability that an aircraft arriving during a particular brief period of time (e.g., 15 min) will need to revert to SDA when the controller tentatively plans to permit CDA for all aircrafts arriving during that time period. If this probability is low enough, the controller may plan to permit CDA during that time period. We utilize an analytical approach and queueing theory framework that considers factors such traffic and weather conditions to estimate the probability. We also provide the number of aircrafts that can be accommodated within the airport’s stacking space using CDA. This number provides insight into whether a particular aircraft may use CDA

Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports

Continuous Descent Approach (CDA) is the flight technique for aircraft to continuously descend from cruise altitude with an idle thrust setting and without level-offs, contrary to the staircase-like Step-down Descent Approach (SDA). Important for air transportation sustainability, using CDA reduces noise, fuel consumption, and pollution. Nevertheless, CDA has been limited to low traffic levels at airports, often at night, because it requires more separation distance between aircraft arrivals and, thus, could decrease throughput. Insufficient attention has been given to helping air traffic controllers decide when CDA may be used. In this paper, we calculate the probability that an aircraft arriving during a particular brief period of time (e.g., 15 min) will need to revert to SDA when the controller tentatively plans to permit CDA for all aircrafts arriving during that time period. If this probability is low enough, the controller may plan to permit CDA during that time period. We utilize an analytical approach and queueing theory framework that considers factors such traffic and weather conditions to estimate the probability. We also provide the number of aircrafts that can be accommodated within the airport’s stacking space using CDA. This number provides insight into whether a particular aircraft may use CDA