Alessandro Rozza

IDEA: intrinsic dimension estimation algorithm

The high dimensionality of some real life signals makes the usage of the most common signal processing and pattern recognition methods unfeasible. For this reason, in literature a great deal of research work has been devoted to the development of algorithms performing dimensionality reduction. To this aim, an useful help could be provided by the estimation of the intrinsic dimensionality of a given dataset, that is the minimum number of parameters needed to capture, and describe, all the information carried by the data. Although many techniques have been proposed, most of them fail in case of noisy data or when the intrinsic dimensionality is too high. In this paper we propose a local intrinsic dimension estimator exploiting the statistical properties of data neighborhoods. The algorithm evaluation on both synthetic and real datasets, and the comparison with state of the art algorithms, proves that the proposed technique is promising.

DANCo: An intrinsic dimensionality estimator exploiting angle and norm concentration

Abstract In the past decade the development of automatic intrinsic dimensionality estimators has gained considerable attention due to its relevance in several application fields. However, most of the proposed solutions prove to be not robust on noisy datasets, and provide unreliable results when the intrinsic dimensionality of the input dataset is high and the manifold where the points are assumed to lie is nonlinearly embedded in a higher dimensional space. In this paper we propose a novel intrinsic dimensionality estimator ( DANCo ) and its faster variant ( FastDANCo ), which exploit the information conveyed both by the normalized nearest neighbor distances and by the angles computed on couples of neighboring points. The effectiveness and robustness of the proposed algorithms are assessed by experiments on synthetic and real datasets, by the comparative evaluation with state-of-the-art methodologies, and by significance tests.

Novel IPCA-Based Classifiers and Their Application to Spam Filtering

This paper proposes a novel two-class classifier, called IPCAC, based on the Isotropic Principal Component Analysis technique; it allows to deal with training data drawn from Mixture of Gaussian distributions, by projecting the data on the Fisher subspace that separates the two classes. The obtained results demonstrate that IPCAC is a promising technique; furthermore, to cope with training datasets being dynamically supplied, and to work with non-linearly separable classes, two improvements of this classifier are defined: a model merging algorithm, and a kernel version of IPCAC. The effectiveness of the proposed methods is shown by their application to the spam classification problem, and by the comparison of the achieved results with those obtained by Support Vector Machines SVM, and K-Nearest Neighbors KNN.

PIPCAC: A Novel Binary Classifier Assuming Mixtures of Gaussian Functions

Probabilistic classifiers are among the most popular classification methods adopted by the machine learning community. They are often based on a-priori knowledge about the probability distribution underlying the data; nevertheless this information is rarely provided, so that a family of probability distribution functions is assumed to be an approximation model. In this paper we present an efficient binary classification algorithm, called Perceptron-IPCAC (PIPCAC), assuming that each class is distributed accordingly to a Mixture of Gaussian functions. PIPCAC is defined as a multilayer perceptron trained by combining different linear classifiers. The algorithm has been tested on both synthetic and real datasets, and the obtained results demonstrate the effectiveness and efficiency of the proposed method. Furthermore, the promising performances have been confirmed by the comparison of its results with those achieved by Support Vector Machines.

Local Intrinsic Dimensionality Based Features for Clustering

One of the fundamental tasks of unsupervised learning is dataset clustering, to partition the input dataset into clusters composed by somehow “similar” objects that “differ” from the objects belonging to other classes. To this end, in this paper we assume that the different clusters are drawn from different, possibly intersecting, geometrical structures represented by manifolds embedded into a possibly higher dimensional space. Under these assumptions, and considering that each manifold is typified by a geometrical structure characterized by its intrinsic dimensionality, which (possibly) differs from the intrinsic dimensionalities of other manifolds, we code the input data by means of local intrinsic dimensionality estimates and features related to them, and we subsequently apply simple and basic clustering algorithms, since our interest is specifically aimed at assessing the discriminative power of the proposed features. Indeed, their encouraging discriminative quality is shown by a feature relevance test, by the clustering results achieved on both synthetic and real datasets, and by their comparison to those obtained by related and classical state-of-the-art clustering approaches.

A Novel Ensemble Technique for Protein Subcellular Location Prediction

In this chapter we present an ensemble classifier that performs multi-class classification by combining several kernel classifiers through Decision Direct Acyclic Graph (DDAG). Each base classifier, called K-TIPCAC, is mainly based on the projection of the given points on the Fisher subspace, estimated on the training data, by means of a novel technique. The proposed multiclass classifier is applied to the task of protein subcellular location prediction, which is one of the most difficult multiclass prediction problems in modern computational biology. Although many methods have been proposed in the literature to solve this problem all the existing approaches are affected by some limitations, so that the problem is still open. Experimental results clearly indicate that the proposed technique, called DDAG K-TIPCAC, performs equally, if not better, than state of the art ensemble methods aimed at multi-class classification of highly unbalanced data.

Real-Time Age Estimation from Face Imagery Using Fisher Vectors

In the last decade facial age estimation has grown its importance in computer vision. In this paper we propose an efficient and effective age estimation system from face imagery. To assess the quality of the proposed approach we compare the results obtained by our system with those achieved by other recently published methods on a very large dataset of more than 55K images of people with different gender and ethnicity. These results show how a carefully engineered pipeline of efficient image analysis and pattern recognition techniques leads to state-of-the-art results at 20FPS using a single thread on a 1.6GHZ i5-2467M processor.

A Novel Intrinsic Dimensionality Estimator Based on Rank-Order Statistics

In the past two decades the estimation of the intrinsic dimensionality of a dataset has gained considerable importance, since it is a relevant information for several real life applications. Unfortunately, although a great deal of research effort has been devoted to the development of effective intrinsic dimensionality estimators, the problem is still open. For this reason, in this paper we propose a novel robust intrinsic dimensionality estimator that exploits the information conveyed by the normalized nearest neighbor distances, through a technique based on rank-order statistics that limits common underestimation issues related to the edge effect. Experiments performed on both synthetic and real datasets highlight the robustness and the effectiveness of the proposed algorithm when compared to state-of-the-art methodologies.

Automatic alignment of Gel 2D Images

Proteomics has gained a wide interest in the last decade since it involves the comparative study of protein expressions to identify bio-markers for early diagnosis of unpredictable, and serious, pathologies. The most powerful techniques for protein investigation compare 2D gel electrophoresys images that represent the protein composition of healthy and diseased tissues. Nevertheless, this analysis is problematic since gel images are affected by high noise levels and they are distorted, so that the same protein spot has different locations on different gels. Furthermore, the acquisition of a statistically significant sample of gels from a unique laboratory is problematic due to ethical problems, to the rarity of certain diseases, and to the fact that the process of gel electrophoresys is time consuming and costly. However, a great deal of information is present in the scientific literature in the form of images reporting 2D gels acquired for different experiments, we have developed a framework to compare annotated 2D gel images extracted from state of the art, and publicly available papers. The system has been assessed by performing the comparative analysis of the Haptoglobin; although the analyzed images are much more noisy and distorted than their sources the system achieves promising results.