Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical to low-dimension vectors. The current hash-based methods do not provide any guarantee on the Hamming distances between compressed representations. Here we present FSketch create sketches for sparse data and an estimator estimate pairwise among uncompres...

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest dimensionality reduction techniques. In order these methods to improve performance gains and understanding of underlying proper metric needs be identified. This step often overlooked, metrics are typically chosen without consideration geometry data. this paper, we present method incorporating elas...

The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These gather several data features interest, such as dynamical structure, input-output relationships, the correlation between sets, covariance, etc. Dimensionality entails mapping set onto low Motivated by lack learning models’ performance due to data, this study encounters five di...

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Dimensionality reduction is a very common preprocessing approach in many machine learning tasks. The goal is to design data representations that on one hand reduce the dimension of the data (therefore allowing faster processing), and on the other hand aim to retain as much task-relevant information as possible. We look at generic dimensionality reduction approaches that do not rely on much task...

Previously in the course, we have discussed algorithms suited for a large number of data points. This lecture discusses when the dimensionality of the data points becomes large. We denote the data set as x1, x2, . . . , xn ∈ RD for D >> n, and will consider dimensionality reductions f : RD → Rd for d << D. We would like the function f to preserve some properties of the original data set, such a...