2) Matrix Factorization
Matrix factorization approach is one more representation of pLSA. The word frequency matrix that defines the dataset is a very large and sparse matrix; it has number of rows equal to the documents d, and the number of columns is the number of different words k that appear in our corpus. The reason for sparseness is because only a small percentage of the words are used in each document depending on its particular topic. So, dimensionality reduction is an issue for word frequency matrix as most of its entries are null providing no specific detailing. This can be attained by approximating the co-occurrence matrix (denoted by F) as a product of two low-rank (thinner) matrices P and R. For example:
F≈F ̂=P.R
So, if the size of P is X×Y and the size of R is Y×Z, with Y≪Z,X , this will accomplish the dimensionality reduction, as X ∙"Z"≫ "X∙Y+Y∙Z" . Also matrices P and R indicate some details about the latent structure of the data. pLSA exactly performs a matrix factorization of the conditional distribution P(w|d).
F = "P∙Q∙R" where,
P consists of the document probabilities P(d|z).
Q is a diagonal matrix of the prior probabilities of the topics P(z).
R relate with the word probability P(w|z).
These matrices represent probability distribution and thus are non-negative and normalized.
pLSA: procedural view
The significant and accurate result from pLSA has increased its outstanding convention in regular practices. Topic Detection and Tracking Corpus in Word Usage Analysis, Image Classification Model etc are some instances of pLSA usage. There are primary steps in Scene Classification that are Training and Testing.
Fig 3. The complete pLSA formulation design defining its primary stages: Training on images, BOW f...
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Text mining, data mining and machine learning algorithms are in great demand in the field of bioinformatics. Text mining techniques applied to bioinformatics importantly involve methods like -
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...been answered. The authors address these problems in this current paper empirical study of plsa and lda.
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To combat these and other issues that can arise due to a lack of training, the development of a training program will wan...
9 Fayyad U., Piatetsky-Shapiro G., Smyth, Padhraic - "The KDD Process for Extracting Useful Knowledge from volumes of Data" - Communications of the ACM vol. 39, no. 11 (Nov. 1996).
Sentiment analysis known also as polarity classification , subjectively analysis, opinion mining, affect analysis, its relishing field of study that that deal with people’s opinions, sentiment , emotions and attitudes about different entities such as products ,service ,individuals ,companies ,events and topics; and includes many fields like natural language process, machine learning, computational linguistic ,statistics, and artificial intelligence . it’s a set of computational and natural language techniques which could be leveraged in order to extract subject information in a given text .
Rearranging this formula gives a phrase for dimension relying on how the size alters as a function of linear scaling:
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
A matrix is a system in which m.n elements are arranged in a rectangular formation of m rows and n columns bounded by brackets [].