Semi supervised weighted maximum variance dimensionality reduction

In the recent years, we have huge amounts of data which we want to classify with minimal human intervention. Only few features from the data that is available might be useful in some scenarios. In those scenarios, the dimensionality reduction methods play a major role for extracting useful features. The two parameter weighted maximum variance (2P-WMV) is a generalized dimensionality reduction method of which principal component analysis (PCA) and maximum margin criterion (MMC) are special cases.. In this paper, we have extended the 2P-WMV approach from our previous work to a semi-supervised version. The objective of this work is specially to show how two parameter version of Weighted Maximum Variance (2P-WMV) performs in Semi-Supervised environment in comparison to the supervised learning. By making use of both labeled and unlabeled data, we present our method with experimental results on several datasets using various approaches

Contribution to Graph-based Manifold Learning with Application to Image Categorization.

122 pLos algoritmos de aprendizaje de variedades basados en grafos (Graph,based manifold) son técnicas que han demostrado ser potentes herramientas para la extracción de características y la reducción de la dimensionalidad en los campos de reconomiento de patrones, visión por computador y aprendizaje automático. Estos algoritmos utilizan información basada en las similitudes de pares de muestras y del grafo ponderado resultante para revelar la estructura geométrica intrínseca de la variedad

Unsupervised feature selection by means of external validity indices

Feature selection for unsupervised data is a difficult task because a reference partition is not available to evaluate the relevance of the features. Recently, different proposals of methods for consensus clustering have used external validity indices to assess the agreement among partitions obtained by clustering algorithms with different parameter values. Theses indices are independent of the characteristics of the attributes describing the data, the way the partitions are represented or the shape of the clusters. This independence allows to use these measures to assess the similarity of partitions with different subsets of attributes. As for supervised feature selection, the goal of unsupervised feature selection is to maintain the same patterns of the original data with less information. The hypothesis of this paper is that the clustering of the dataset with all the attributes, even when its quality is not perfect, can be used as the basis of the heuristic exploration the space of subsets of features. The proposal is to use external validation indices as the specific measure used to assess well this information is preserved by a subset of the original attributes. Different external validation indices have been proposed in the literature. This paper will present experiments using the adjusted Rand, Jaccard and Folkes&Mallow indices. Artificially generated datasets will be used to test the methodology with different experimental conditions such as the number of clusters, cluster spatial separanton and the ratio of irrelevant features. The methodology will also be applied to real datasets chosen from the UCI machine learning datasets repository.Preprin

Contribution to Graph-based Manifold Learning with Application to Image Categorization.

122 pLos algoritmos de aprendizaje de variedades basados en grafos (Graph,based manifold) son técnicas que han demostrado ser potentes herramientas para la extracción de características y la reducción de la dimensionalidad en los campos de reconomiento de patrones, visión por computador y aprendizaje automático. Estos algoritmos utilizan información basada en las similitudes de pares de muestras y del grafo ponderado resultante para revelar la estructura geométrica intrínseca de la variedad

Aco-based feature selection algorithm for classification

Dataset with a small number of records but big number of attributes represents a phenomenon called “curse of dimensionality”. The classification of this type of dataset requires Feature Selection (FS) methods for the extraction of useful information. The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. An enhanced graph clustering ant colony optimisation (EGCACO) algorithm is proposed to solve the three (3) MGCACO algorithm problems. The proposed improvement includes: (i) an ACO feature clustering method to obtain clusters of highly correlated features; (ii) an adaptive selection technique for subset construction from the clusters of features; and (iii) a genetic-based method for producing the final subset of features. The ACO feature clustering method utilises the ability of various mechanisms such as intensification and diversification for local and global optimisation to provide highly correlated features. The adaptive technique for ant selection enables the parameter to adaptively change based on the feedback of the search space. The genetic method determines the final subset, automatically, based on the crossover and subset quality calculation. The performance of the proposed algorithm was evaluated on 18 benchmark datasets from the University California Irvine (UCI) repository and nine (9) deoxyribonucleic acid (DNA) microarray datasets against 15 benchmark metaheuristic algorithms. The experimental results of the EGCACO algorithm on the UCI dataset are superior to other benchmark optimisation algorithms in terms of the number of selected features for 16 out of the 18 UCI datasets (88.89%) and the best in eight (8) (44.47%) of the datasets for classification accuracy. Further, experiments on the nine (9) DNA microarray datasets showed that the EGCACO algorithm is superior than the benchmark algorithms in terms of classification accuracy (first rank) for seven (7) datasets (77.78%) and demonstrates the lowest number of selected features in six (6) datasets (66.67%). The proposed EGCACO algorithm can be utilised for FS in DNA microarray classification tasks that involve large dataset size in various application domains

Learning Feature Weights for Density-Based Clustering

K-Means is the most popular and widely used clustering algorithm. This algorithm cannot recover non-spherical shape clusters in data sets. DBSCAN is arguably the most popular algorithm to recover arbitrary shape clusters; this is why this density-based clustering algorithm is of great interest to tackle its weaknesses. One issue of concern is that DBSCAN requires two parameters, and it cannot recover widely variable density clusters. The problem lies at the heart of this thesis is that during the clustering process DBSCAN takes all the available features and treats all the features equally regardless of their degree of relevance in the data set, which can have negative impacts. This thesis addresses the above problems by laying the foundation of the feature weighted density-based clustering. Specifically, the thesis introduces a densitybased clustering algorithm using reverse nearest neighbour, DBSCANR that require less parameter than DBSCAN for recovering clusters. DBSCANR is based on the insight that in real-world data sets the densities of arbitrary shape clusters to be recovered within a data set are very different from each other. The thesis extends DBSCANR to what is referred to as weighted DBSCANR, WDBSCANR by exploiting feature weighting technique to give the different level of relevance to the features in a data set. The thesis extends W-DBSCANR further by using the Minkowski metric so that the weight can be interpreted as feature re-scaling factors named MW-DBSCANR. Experiments on both artificial and realworld data sets demonstrate the superiority of our method over DBSCAN type algorithms. These weighted algorithms considerably reduce the impact of irrelevant features while recovering arbitrary shape clusters of different level of densities in a high-dimensional data set. Within this context, this thesis incorporates a popular algorithm, feature selection using feature similarity, FSFS into bothW-DBSCANR andMW-DBSCANR, to address the problem of feature selection. This unsupervised feature selection algorithm makes use of feature clustering and feature similarity to reduce the number of features in a data set. With a similar aim, exploiting the concept of feature similarity, the thesis introduces a method, density-based feature selection using feature similarity, DBFSFS to take density-based cluster structure into consideration for reducing the number of features in a data set. This thesis then applies the developed method to real-world high-dimensional gene expression data sets. DBFSFS improves the clustering recovery by substantially reducing the number of features from high-dimensional low sample size data sets

Laplacian linear discriminant analysis approach to unsupervised feature selection.

Until recently, numerous feature selection techniques have been proposed and found wide applications in genomics and proteomics. For instance, feature/gene selection has proven to be useful for biomarker discovery from microarray and mass spectrometry data. While supervised feature selection has been explored extensively, there are only a few unsupervised methods that can be applied to exploratory data analysis. In this paper, we address the problem of unsupervised feature selection. First, we extend Laplacian linear discriminant analysis (LLDA) to unsupervised cases. Second, we propose a novel algorithm for computing LLDA, which is efficient in the case of high dimensionality and small sample size as in microarray data. Finally, an unsupervised feature selection method, called LLDA-based Recursive Feature Elimination (LLDA-RFE), is proposed. We apply LLDA-RFE to several public data sets of cancer microarrays and compare its performance with those of Laplacian score and SVD-entropy, two state-of-the-art unsupervised methods, and with that of Fisher score, a supervised filter method. Our results demonstrate that LLDA-RFE outperforms Laplacian score and shows favorable performance against SVD-entropy. It performs even better than Fisher score for some of the data sets, despite the fact that LLDA-RFE is fully unsupervised