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Discriminative Components of Data
Oleh:
Kaski, S.
;
Peltonen, J.
Jenis:
Article from Journal - ilmiah internasional
Dalam koleksi:
IEEE Transactions on Neural Networks vol. 16 no. 1 (Jan. 2005)
,
page 68-83.
Topik:
task components
;
discriminative
;
components
;
data
Ketersediaan
Perpustakaan Pusat (Semanggi)
Nomor Panggil:
II36.12
Non-tandon:
1 (dapat dipinjam: 0)
Tandon:
tidak ada
Lihat Detail Induk
Isi artikel
A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to : 1) maximizing mutual information with the classes, and 2) finding principal components in the so called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy - based alternative while having essentially equivalent computational cost.
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